From 50b2b0208193245992d2de0f756f7ef4a210a82d Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Wed, 6 Mar 2024 15:42:37 -0500 Subject: [PATCH 01/21] Add yolox base models --- yolox-burn/LICENSE-APACHE | 1 + yolox-burn/LICENSE-MIT | 1 + yolox-burn/NOTICES.md | 16 ++ yolox-burn/README.md | 44 +++++ yolox-burn/examples/inference.rs | 57 ++++++ yolox-burn/samples/dog.jpg | Bin 0 -> 4055302 bytes yolox-burn/src/lib.rs | 3 + yolox-burn/src/model/blocks.rs | 245 ++++++++++++++++++++++++ yolox-burn/src/model/bottleneck.rs | 240 ++++++++++++++++++++++++ yolox-burn/src/model/darknet.rs | 170 +++++++++++++++++ yolox-burn/src/model/head.rs | 235 +++++++++++++++++++++++ yolox-burn/src/model/mod.rs | 7 + yolox-burn/src/model/pafpn.rs | 168 +++++++++++++++++ yolox-burn/src/model/weights.rs | 128 +++++++++++++ yolox-burn/src/model/yolox.rs | 289 +++++++++++++++++++++++++++++ 15 files changed, 1604 insertions(+) create mode 120000 yolox-burn/LICENSE-APACHE create mode 120000 yolox-burn/LICENSE-MIT create mode 100644 yolox-burn/NOTICES.md create mode 100644 yolox-burn/README.md create mode 100644 yolox-burn/examples/inference.rs create mode 100644 yolox-burn/samples/dog.jpg create mode 100644 yolox-burn/src/lib.rs create mode 100644 yolox-burn/src/model/blocks.rs create mode 100644 yolox-burn/src/model/bottleneck.rs create mode 100644 yolox-burn/src/model/darknet.rs create mode 100644 yolox-burn/src/model/head.rs create mode 100644 yolox-burn/src/model/mod.rs create mode 100644 yolox-burn/src/model/pafpn.rs create mode 100644 yolox-burn/src/model/weights.rs create mode 100644 yolox-burn/src/model/yolox.rs diff --git a/yolox-burn/LICENSE-APACHE b/yolox-burn/LICENSE-APACHE new file mode 120000 index 0000000..965b606 --- /dev/null +++ b/yolox-burn/LICENSE-APACHE @@ -0,0 +1 @@ +../LICENSE-APACHE \ No newline at end of file diff --git a/yolox-burn/LICENSE-MIT b/yolox-burn/LICENSE-MIT new file mode 120000 index 0000000..76219eb --- /dev/null +++ b/yolox-burn/LICENSE-MIT @@ -0,0 +1 @@ +../LICENSE-MIT \ No newline at end of file diff --git a/yolox-burn/NOTICES.md b/yolox-burn/NOTICES.md new file mode 100644 index 0000000..776afb6 --- /dev/null +++ b/yolox-burn/NOTICES.md @@ -0,0 +1,16 @@ +# NOTICES AND INFORMATION + +This file contains notices and information required by libraries that this repository copied or derived from. The use of the following resources complies with the licenses provided. + +## Sample Image + +Image Title: This is our Mascot DeVille. +Author: Adelaidasofia +Source: https://commons.wikimedia.org/wiki/File:VillyCustomsMascotDeVille%22Villy%22.jpg +License: https://creativecommons.org/licenses/by-sa/3.0/ + +## Pre-trained Model + +The COCO pre-trained model was ported from the original [YOLOX implementation](https://github.com/Megvii-BaseDetection/YOLOX). + +As opposed to other YOLO variants (YOLOv8, YOLO-NAS, etc.), both the code and pre-trained weights are distributed under the [Apache 2.0](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/LICENSE) open source license. diff --git a/yolox-burn/README.md b/yolox-burn/README.md new file mode 100644 index 0000000..45a2f10 --- /dev/null +++ b/yolox-burn/README.md @@ -0,0 +1,44 @@ +# YOLOX Burn + +There have been many different object detection models with the YOLO prefix released in the recent +years, though most of them carry a GPL or AGPL license which restricts their usage. For this reason, +we selected [YOLOX](https://arxiv.org/abs/2107.08430) as the first object detection architecture +since both the original code and pre-trained weights are released under the +[Apache 2.0](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/LICENSE) open source license. + +You can find the [Burn](https://github.com/tracel-ai/burn) implementation for the YOLOX variants in +[src/model/yolox.rs](src/model/yolox.rs). + +The model is [no_std compatible](https://docs.rust-embedded.org/book/intro/no-std.html). + +## Usage + +### `Cargo.toml` + +Add this to your `Cargo.toml`: + +```toml +[dependencies] +yolox-burn = { git = "https://github.com/burn-rs/models", package = "yolox-burn", default-features = false } +``` + +If you want to get the pre-trained ImageNet weights, enable the `pretrained` feature flag. + +```toml +[dependencies] +yolox-burn = { git = "https://github.com/burn-rs/models", package = "yolox-burn", features = ["pretrained"] } +``` + +**Important:** this feature requires `std`. + +### Example Usage + +The [inference example](examples/inference.rs) initializes a YOLOX-Nano from the COCO +[pre-trained weights](https://github.com/Megvii-BaseDetection/YOLOX?tab=readme-ov-file#standard-models) +with the `NdArray` backend and performs inference on the provided input image. + +You can run the example with the following command: + +```sh +cargo run --release --features pretrained --example inference samples/dog.jpg +``` diff --git a/yolox-burn/examples/inference.rs b/yolox-burn/examples/inference.rs new file mode 100644 index 0000000..d80ca47 --- /dev/null +++ b/yolox-burn/examples/inference.rs @@ -0,0 +1,57 @@ +use yolox_burn::model::{weights, yolox::Yolox}; + +use burn::{ + backend::NdArray, + tensor::{backend::Backend, Data, Device, Element, Shape, Tensor}, +}; + +const HEIGHT: usize = 640; +const WIDTH: usize = 640; + +fn to_tensor( + data: Vec, + shape: [usize; 3], + device: &Device, +) -> Tensor { + Tensor::::from_data(Data::new(data, Shape::new(shape)).convert(), device) + // permute(2, 0, 1) + .swap_dims(2, 1) // [H, C, W] + .swap_dims(1, 0) // [C, H, W] +} + +pub fn main() { + // Parse arguments + let img_path = std::env::args().nth(1).expect("No image path provided"); + + // Create YOLOX-Nano + let device = Default::default(); + let model: Yolox = Yolox::yolox_s_pretrained(weights::YoloxS::Coco, &device) + .map_err(|err| format!("Failed to load pre-trained weights.\nError: {err}")) + .unwrap(); + + // Load image + let img = image::open(&img_path) + .map_err(|err| format!("Failed to load image {img_path}.\nError: {err}")) + .unwrap(); + + // Resize to 640x640 + let resized_img = img.resize_exact( + WIDTH as u32, + HEIGHT as u32, + image::imageops::FilterType::Triangle, // also known as bilinear in 2D + ); + + // Create tensor from image data + let x = to_tensor( + resized_img.into_rgb8().into_raw(), + [HEIGHT, WIDTH, 3], + &device, + ) + .unsqueeze::<4>(); // [B, C, H, W] + + // Forward pass + let out = model.forward(x); + + // TODO: post-processing and display + println!("Output shape: {:?}", out.shape()); +} diff --git a/yolox-burn/samples/dog.jpg b/yolox-burn/samples/dog.jpg new file mode 100644 index 0000000000000000000000000000000000000000..f748a5da0f46c2b7fc349c0326a9e542ab76f4ec GIT binary patch literal 4055302 zcmeFY1z1*1zc0LQxakrE1w}$YQW^xLI|QT~q`SKW5ew-QBt$?OM7l(!yBk5eLAv40 ztv)`!&%5_N-~P_I&Uc;b`UYmrZ~fS$`3jOeS=;9jhA3_zxaV>7~5|> z-(l=OFyRkX{D0A5gh-eF=6_}h!GG$6Q9}>`8O47cAO#sYMFmJqQSsh=8BGcmHdadLR$Hq&+#?Hsi$;ZhF$r~C|D5_9s zQn0c9;DIOu68LIZZ;tnBQp;mCiL6{#s4{u_@) zz<%<;kdUDWq~AI>BH<9`PoI(YBT;_S&m+-(=?66(iT(=%#z>4`m>6Kpzxa_+BYhD1 zaf2WT88`Cj4_gl*2>;cazkCBV^al1z7PRLZq`&GL@68+Jzc5xs+E0EMqT$ni`V0f? zQ2Jl82#oZ%Kk3MSVEAAD5L`(|`9&uM{HVV$=ojgKlqLPIvLJL~{-Fo^Zw#^!0n`!F z3P@=Y_(v;(hGf4(!N$wS3I17sw(LJ>fEUrnVHXz{X9zhEf5?NB9dWND)!gGEM$ z0SpQVx&^vA+K`4ZiAdfS1JvJK_GA#W1UmmOZV2TsC!q^abucz&)}Oi|DkE;HOaA_kBxEI? zLbCTn5M=)c-~y&gPmvKO=;vsuO1n3Bi2$x_mWQU*kc<-@lp?sJM>=@}SvN#L| 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zd!+?`-65&=ltYKl)qL2s)_c%>aVGuW$Mn@t(%XQZrz&^^L`&!-T;$nKY(ne;@L2T^ zaDXrV(haR?1j+LEe#+8$;la`W)OA98mBy8ds~{X_$DUEsLN{KG23hQ`4Qg?2Jt7W& z_#f~IuRlO0PyQQo*u4?h_TL>t)9lmae(L+PJUZIL(X267`7hsLR#?GFG!PnBmNrn} zeT1<~lI1nmUU2&9?ajbX%bDY}e)sUwQJ91&ii8w-O96`*&%z1Dc)x6qx}HZz>YG}^ zR-PGW-}?3?wMS%tpI>~nIY~#Hln-Cr62B$f zK^BhtJs@-noY3sZBwC_(N>a1~Ul`3aPSPcbKk1P8WYLzNyAMM-EC6hLe`-%;TLy`ajk=Fs50(vWjKM}-SP(nli81v|M^@4Q$ z`+avj&V9Pnb$n8`FvWuClJ`S-pqtoCv>Ty)HB?o_`K7n#DHyWc*cVWN9@XH_qr>9O z_dufGj>eqg`gem?3D(2&bydzBno^!nvMeZbb27X_z@}tF6Qu$PyY`;snuct literal 0 HcmV?d00001 From 9a8851c6f10c81faf05e4c4ee629d535a289c2e1 Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Fri, 8 Mar 2024 11:02:14 -0500 Subject: [PATCH 04/21] Add post-processing and inference results --- yolox-burn/.gitignore | 2 + yolox-burn/examples/inference.rs | 99 ++++++++++++++++++++-- yolox-burn/src/model/boxes.rs | 139 +++++++++++++++++++++++++++++++ yolox-burn/src/model/mod.rs | 3 + 4 files changed, 238 insertions(+), 5 deletions(-) create mode 100644 yolox-burn/.gitignore create mode 100644 yolox-burn/src/model/boxes.rs diff --git a/yolox-burn/.gitignore b/yolox-burn/.gitignore new file mode 100644 index 0000000..ea6f423 --- /dev/null +++ b/yolox-burn/.gitignore @@ -0,0 +1,2 @@ +# Output image +*.output.png diff --git a/yolox-burn/examples/inference.rs b/yolox-burn/examples/inference.rs index d80ca47..3541379 100644 --- a/yolox-burn/examples/inference.rs +++ b/yolox-burn/examples/inference.rs @@ -1,7 +1,10 @@ -use yolox_burn::model::{weights, yolox::Yolox}; +use std::path::Path; + +use image::{DynamicImage, ImageBuffer}; +use yolox_burn::model::{boxes::nms, weights, yolox::Yolox, BoundingBox}; use burn::{ - backend::NdArray, + backend::LibTorch, tensor::{backend::Backend, Data, Device, Element, Shape, Tensor}, }; @@ -19,13 +22,82 @@ fn to_tensor( .swap_dims(1, 0) // [C, H, W] } +/// Draws bounding boxes on the given image. +/// +/// # Arguments +/// +/// * `image`: Original input image. +/// * `boxes` - Bounding boxes, grouped per class. +/// * `color` - [R, G, B] color values to draw the boxes. +/// * `ratio` - [x, y] aspect ratio to scale the predicted boxes. +/// +/// # Returns +/// +/// The image annotated with bounding boxes. +fn draw_boxes( + image: DynamicImage, + boxes: &[Vec], + color: &[u8; 3], + ratio: &[f32; 2], // (x, y) ratio +) -> DynamicImage { + // Assumes x1 <= x2 and y1 <= y2 + fn draw_rect( + image: &mut ImageBuffer, Vec>, + x1: u32, + x2: u32, + y1: u32, + y2: u32, + color: &[u8; 3], + ) { + for x in x1..=x2 { + let pixel = image.get_pixel_mut(x, y1); + *pixel = image::Rgb(*color); + let pixel = image.get_pixel_mut(x, y2); + *pixel = image::Rgb(*color); + } + for y in y1..=y2 { + let pixel = image.get_pixel_mut(x1, y); + *pixel = image::Rgb(*color); + let pixel = image.get_pixel_mut(x2, y); + *pixel = image::Rgb(*color); + } + } + + // Annotate the original image and print boxes information. + let (image_h, image_w) = (image.height(), image.width()); + let mut image = image.to_rgb8(); + for (class_index, bboxes_for_class) in boxes.iter().enumerate() { + for b in bboxes_for_class.iter() { + let xmin = (b.xmin * ratio[0]).clamp(0., image_w as f32 - 1.); + let ymin = (b.ymin * ratio[1]).clamp(0., image_h as f32 - 1.); + let xmax = (b.xmax * ratio[0]).clamp(0., image_w as f32 - 1.); + let ymax = (b.ymax * ratio[1]).clamp(0., image_h as f32 - 1.); + + println!( + "Predicted {} ({:.2}) at [{:.2}, {:.2}, {:.2}, {:.2}]", + class_index, b.confidence, xmin, ymin, xmax, ymax, + ); + + draw_rect( + &mut image, + xmin as u32, + xmax as u32, + ymin as u32, + ymax as u32, + color, + ); + } + } + DynamicImage::ImageRgb8(image) +} + pub fn main() { // Parse arguments let img_path = std::env::args().nth(1).expect("No image path provided"); // Create YOLOX-Nano let device = Default::default(); - let model: Yolox = Yolox::yolox_s_pretrained(weights::YoloxS::Coco, &device) + let model: Yolox = Yolox::yolox_s_pretrained(weights::YoloxS::Coco, &device) .map_err(|err| format!("Failed to load pre-trained weights.\nError: {err}")) .unwrap(); @@ -52,6 +124,23 @@ pub fn main() { // Forward pass let out = model.forward(x); - // TODO: post-processing and display - println!("Output shape: {:?}", out.shape()); + // Post-processing + let [_, num_boxes, num_outputs] = out.shape().dims; + let boxes = out.clone().slice([0..1, 0..num_boxes, 0..4]); + let obj_scores = out.clone().slice([0..1, 0..num_boxes, 4..5]); + let cls_scores = out.slice([0..1, 0..num_boxes, 5..num_outputs]); + let scores = cls_scores * obj_scores; + let boxes = nms(boxes, scores, 0.65, 0.5); + + // Draw outputs and save results + let (h, w) = (img.height(), img.width()); + let img_out = draw_boxes( + img, + &boxes[0], + &[239u8, 62u8, 5u8], + &[w as f32 / WIDTH as f32, h as f32 / HEIGHT as f32], + ); + + let img_path = Path::new(&img_path); + let _ = img_out.save(img_path.with_extension("output.png")); } diff --git a/yolox-burn/src/model/boxes.rs b/yolox-burn/src/model/boxes.rs new file mode 100644 index 0000000..47a99e6 --- /dev/null +++ b/yolox-burn/src/model/boxes.rs @@ -0,0 +1,139 @@ +use burn::tensor::{backend::Backend, ElementConversion, Tensor}; +use itertools::Itertools; + +pub struct BoundingBox { + pub xmin: f32, + pub ymin: f32, + pub xmax: f32, + pub ymax: f32, + pub confidence: f32, +} + +/// Non-maximum suppression (NMS) filters overlaping bounding boxes that have an intersection-over- +/// union (IoU) greater or equal than the specified `iou_threshold` with previously selected boxes. +/// +/// Boxes are filtered based on `score_threshold` and ranked based on their score. As such, lower +/// scoring boxes are removed when overlapping with another (higher scoring) box. +/// +/// # Arguments +/// +/// * `boxes`: Bounding box coordinates. Shape: `[batch_size, num_boxes, 4]`. +/// * `scores` - Classification scores for each box. Shape: `[batch_size, num_boxes, num_classes]`. +/// * `iou_threshold` - Scalar threshold for IoU. +/// * `score_threshold` - Scalar threshold for scores. +/// +/// # Returns +/// +/// Vector of bounding boxes grouped by class for each batch. The boxes are sorted in decreasing +/// order of scores for each class. +pub fn nms( + boxes: Tensor, + scores: Tensor, + iou_threshold: f32, + score_threshold: f32, +) -> Vec>> { + let [batch_size, num_boxes, num_classes] = scores.shape().dims; + + // Bounding boxes grouped by batch and by (maximum) class index + let mut bboxes = boxes + .iter_dim(0) + .zip(scores.iter_dim(0)) + .enumerate() + // Per-batch + .map(|(_, (candidate_boxes, candidate_scores))| { + // Keep max scoring boxes only ([num_boxes, 1], [num_boxes, 1]) + let (cls_score, cls_idx) = candidate_scores.squeeze::<2>(0).max_dim_with_indices(1); + let cls_score: Vec<_> = cls_score + .into_data() + .value + .iter() + .map(|v| v.elem::()) + .collect(); + let cls_idx: Vec<_> = cls_idx + .into_data() + .value + .iter() + .map(|v| v.elem::() as usize) + .collect(); + + // [num_boxes, 4] + let candidate_boxes: Vec<_> = candidate_boxes + .into_data() + .value + .iter() + .map(|v| v.elem::()) + .collect(); + + // Per-class filtering based on score + (0..num_classes) + .map(|cls_id| { + // [num_boxes, 1] + (0..num_boxes) + .filter_map(|box_idx| { + let box_cls_idx = cls_idx[box_idx]; + if box_cls_idx != cls_id { + return None; + } + let box_cls_score = cls_score[box_idx]; + if box_cls_score >= score_threshold { + let bbox = &candidate_boxes[box_idx * 4..box_idx * 4 + 4]; + // println!("Candidate {}: {:?}", box_idx, bbox); + Some(BoundingBox { + xmin: bbox[0] - bbox[2] / 2., + ymin: bbox[1] - bbox[3] / 2., + xmax: bbox[0] + bbox[2] / 2., + ymax: bbox[1] + bbox[3] / 2., + confidence: box_cls_score, + }) + } else { + None + } + }) + .sorted_unstable_by(|a, b| a.confidence.partial_cmp(&b.confidence).unwrap()) + .collect::>() + }) + .collect::>() + }) + .collect::>(); + + for batch_idx in 0..batch_size { + non_maximum_suppression(&mut bboxes[batch_idx], iou_threshold); + } + + bboxes +} + +/// Intersection over union of two bounding boxes. +pub fn iou(b1: &BoundingBox, b2: &BoundingBox) -> f32 { + let b1_area = (b1.xmax - b1.xmin + 1.) * (b1.ymax - b1.ymin + 1.); + let b2_area = (b2.xmax - b2.xmin + 1.) * (b2.ymax - b2.ymin + 1.); + let i_xmin = b1.xmin.max(b2.xmin); + let i_xmax = b1.xmax.min(b2.xmax); + let i_ymin = b1.ymin.max(b2.ymin); + let i_ymax = b1.ymax.min(b2.ymax); + let i_area = (i_xmax - i_xmin + 1.).max(0.) * (i_ymax - i_ymin + 1.).max(0.); + i_area / (b1_area + b2_area - i_area) +} + +/// Perform non-maximum suppression over boxes of the same class. +pub fn non_maximum_suppression(bboxes: &mut [Vec], threshold: f32) { + for bboxes_for_class in bboxes.iter_mut() { + bboxes_for_class.sort_by(|b1, b2| b2.confidence.partial_cmp(&b1.confidence).unwrap()); + let mut current_index = 0; + for index in 0..bboxes_for_class.len() { + let mut drop = false; + for prev_index in 0..current_index { + let iou = iou(&bboxes_for_class[prev_index], &bboxes_for_class[index]); + if iou > threshold { + drop = true; + break; + } + } + if !drop { + bboxes_for_class.swap(current_index, index); + current_index += 1; + } + } + bboxes_for_class.truncate(current_index); + } +} diff --git a/yolox-burn/src/model/mod.rs b/yolox-burn/src/model/mod.rs index 1649d47..ed5731d 100644 --- a/yolox-burn/src/model/mod.rs +++ b/yolox-burn/src/model/mod.rs @@ -1,7 +1,10 @@ mod blocks; mod bottleneck; +pub mod boxes; mod darknet; mod head; mod pafpn; pub mod weights; pub mod yolox; + +pub use boxes::BoundingBox; From 6d341bd05d6952896a43016052975b08187cfe6e Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Fri, 8 Mar 2024 11:05:54 -0500 Subject: [PATCH 05/21] Cleanup --- yolox-burn/src/model/blocks.rs | 3 +-- yolox-burn/src/model/bottleneck.rs | 6 +----- yolox-burn/src/model/boxes.rs | 6 +++--- 3 files changed, 5 insertions(+), 10 deletions(-) diff --git a/yolox-burn/src/model/blocks.rs b/yolox-burn/src/model/blocks.rs index 7ef8f9b..871b298 100644 --- a/yolox-burn/src/model/blocks.rs +++ b/yolox-burn/src/model/blocks.rs @@ -165,8 +165,7 @@ impl Focus { 1, ); - let x = self.conv.forward(x); - x + self.conv.forward(x) } } diff --git a/yolox-burn/src/model/bottleneck.rs b/yolox-burn/src/model/bottleneck.rs index 403a165..d83f0a6 100644 --- a/yolox-burn/src/model/bottleneck.rs +++ b/yolox-burn/src/model/bottleneck.rs @@ -82,7 +82,7 @@ pub struct SppBottleneck { impl SppBottleneck { pub fn forward(&self, x: Tensor) -> Tensor { - if self.m.len() == 0 { + if self.m.is_empty() { panic!("No MaxPool2d modules found"); } @@ -163,10 +163,6 @@ impl CspBottleneck { .m .iter() .fold(x1, |x_i, bottleneck| bottleneck.forward(x_i)); - // let mut x1 = x1; - // for bottleneck in &self.m { - // x1 = bottleneck.forward(x1); - // } let x = Tensor::cat(vec![x1, x2], 1); diff --git a/yolox-burn/src/model/boxes.rs b/yolox-burn/src/model/boxes.rs index 47a99e6..29147cd 100644 --- a/yolox-burn/src/model/boxes.rs +++ b/yolox-burn/src/model/boxes.rs @@ -9,7 +9,7 @@ pub struct BoundingBox { pub confidence: f32, } -/// Non-maximum suppression (NMS) filters overlaping bounding boxes that have an intersection-over- +/// Non-maximum suppression (NMS) filters overlapping bounding boxes that have an intersection-over- /// union (IoU) greater or equal than the specified `iou_threshold` with previously selected boxes. /// /// Boxes are filtered based on `score_threshold` and ranked based on their score. As such, lower @@ -96,8 +96,8 @@ pub fn nms( }) .collect::>(); - for batch_idx in 0..batch_size { - non_maximum_suppression(&mut bboxes[batch_idx], iou_threshold); + for batch_bboxes in bboxes.iter_mut().take(batch_size) { + non_maximum_suppression(batch_bboxes, iou_threshold); } bboxes From b26e6b2e055b34bc408e956dff1d4a6f9c4a28cd Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Fri, 8 Mar 2024 11:20:01 -0500 Subject: [PATCH 06/21] Fix 2d grid repeat with dim=1 --- yolox-burn/src/model/head.rs | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/yolox-burn/src/model/head.rs b/yolox-burn/src/model/head.rs index 41c9777..d2a0429 100644 --- a/yolox-burn/src/model/head.rs +++ b/yolox-burn/src/model/head.rs @@ -27,8 +27,9 @@ fn create_2d_grid(x: usize, y: usize, device: &Device) -> Tensor< .repeat(1, x) .reshape(Shape::new([y, x])); let x_idx = Tensor::arange(0..x as i64, device) + .reshape(Shape::new([1, x])) // can only repeat with dim=1 .repeat(0, y) - .reshape(Shape::new([x, y])); + .reshape(Shape::new([y, x])); Tensor::stack(vec![x_idx, y_idx], 2) } From 86d49f2f29e2ec67d8be36251c731a5bfb40be12 Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Fri, 8 Mar 2024 11:20:27 -0500 Subject: [PATCH 07/21] Default to ndarray backend --- yolox-burn/Cargo.toml | 28 ++++++++++++++++++++++++++++ yolox-burn/examples/inference.rs | 4 ++-- 2 files changed, 30 insertions(+), 2 deletions(-) create mode 100644 yolox-burn/Cargo.toml diff --git a/yolox-burn/Cargo.toml b/yolox-burn/Cargo.toml new file mode 100644 index 0000000..012d0f6 --- /dev/null +++ b/yolox-burn/Cargo.toml @@ -0,0 +1,28 @@ +[package] +authors = ["guillaumelagrange "] +license = "MIT OR Apache-2.0" +name = "yolox-burn" +version = "0.1.0" +edition = "2021" + +[features] +default = [] +std = [] +pretrained = ["burn/network", "std", "dep:dirs"] + +[dependencies] +# Note: default-features = false is needed to disable std +burn = { git = "https://github.com/tracel-ai/burn.git", default-features = false } +burn-import = { git = "https://github.com/tracel-ai/burn.git" } +itertools = { version = "0.12.1", default-features = false, features = ["use_alloc"]} +dirs = { version = "5.0.1", optional = true } +serde = { version = "1.0.192", default-features = false, features = [ + "derive", + "alloc", +] } # alloc is for no_std, derive is needed + +[dev-dependencies] +burn = { git = "https://github.com/tracel-ai/burn.git", features = [ + "ndarray", +] } +image = { version = "0.24.7", features = ["png", "jpeg"] } diff --git a/yolox-burn/examples/inference.rs b/yolox-burn/examples/inference.rs index 3541379..c7bc7e2 100644 --- a/yolox-burn/examples/inference.rs +++ b/yolox-burn/examples/inference.rs @@ -4,7 +4,7 @@ use image::{DynamicImage, ImageBuffer}; use yolox_burn::model::{boxes::nms, weights, yolox::Yolox, BoundingBox}; use burn::{ - backend::LibTorch, + backend::NdArray, tensor::{backend::Backend, Data, Device, Element, Shape, Tensor}, }; @@ -97,7 +97,7 @@ pub fn main() { // Create YOLOX-Nano let device = Default::default(); - let model: Yolox = Yolox::yolox_s_pretrained(weights::YoloxS::Coco, &device) + let model: Yolox = Yolox::yolox_s_pretrained(weights::YoloxS::Coco, &device) .map_err(|err| format!("Failed to load pre-trained weights.\nError: {err}")) .unwrap(); From fa067dfcb92db4c90a9df0547bf3ed8a92837d3c Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Fri, 8 Mar 2024 11:35:50 -0500 Subject: [PATCH 08/21] Switch to YOLOX-tiny for example --- yolox-burn/examples/inference.rs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/yolox-burn/examples/inference.rs b/yolox-burn/examples/inference.rs index c7bc7e2..b504c42 100644 --- a/yolox-burn/examples/inference.rs +++ b/yolox-burn/examples/inference.rs @@ -97,7 +97,7 @@ pub fn main() { // Create YOLOX-Nano let device = Default::default(); - let model: Yolox = Yolox::yolox_s_pretrained(weights::YoloxS::Coco, &device) + let model: Yolox = Yolox::yolox_tiny_pretrained(weights::YoloxTiny::Coco, &device) .map_err(|err| format!("Failed to load pre-trained weights.\nError: {err}")) .unwrap(); From 47708a9c16bc1221d2d83c78752bb07094d7b963 Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Fri, 8 Mar 2024 11:48:06 -0500 Subject: [PATCH 09/21] Remove dead comment --- yolox-burn/src/model/boxes.rs | 1 - 1 file changed, 1 deletion(-) diff --git a/yolox-burn/src/model/boxes.rs b/yolox-burn/src/model/boxes.rs index 29147cd..c459313 100644 --- a/yolox-burn/src/model/boxes.rs +++ b/yolox-burn/src/model/boxes.rs @@ -77,7 +77,6 @@ pub fn nms( let box_cls_score = cls_score[box_idx]; if box_cls_score >= score_threshold { let bbox = &candidate_boxes[box_idx * 4..box_idx * 4 + 4]; - // println!("Candidate {}: {:?}", box_idx, bbox); Some(BoundingBox { xmin: bbox[0] - bbox[2] / 2., ymin: bbox[1] - bbox[3] / 2., From 3b9242005a5b39f1fe95daf01f153420e2db0b79 Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Fri, 8 Mar 2024 14:47:47 -0500 Subject: [PATCH 10/21] Use tensor.dims() --- yolox-burn/examples/inference.rs | 2 +- yolox-burn/src/model/blocks.rs | 2 +- yolox-burn/src/model/boxes.rs | 2 +- yolox-burn/src/model/head.rs | 4 ++-- yolox-burn/src/model/pafpn.rs | 2 +- 5 files changed, 6 insertions(+), 6 deletions(-) diff --git a/yolox-burn/examples/inference.rs b/yolox-burn/examples/inference.rs index b504c42..07c466d 100644 --- a/yolox-burn/examples/inference.rs +++ b/yolox-burn/examples/inference.rs @@ -125,7 +125,7 @@ pub fn main() { let out = model.forward(x); // Post-processing - let [_, num_boxes, num_outputs] = out.shape().dims; + let [_, num_boxes, num_outputs] = out.dims(); let boxes = out.clone().slice([0..1, 0..num_boxes, 0..4]); let obj_scores = out.clone().slice([0..1, 0..num_boxes, 4..5]); let cls_scores = out.slice([0..1, 0..num_boxes, 5..num_outputs]); diff --git a/yolox-burn/src/model/blocks.rs b/yolox-burn/src/model/blocks.rs index 871b298..3832377 100644 --- a/yolox-burn/src/model/blocks.rs +++ b/yolox-burn/src/model/blocks.rs @@ -134,7 +134,7 @@ pub struct Focus { impl Focus { pub fn forward(&self, x: Tensor) -> Tensor { let device = x.device(); - let [_, _, h, w] = x.shape().dims; + let [_, _, h, w] = x.dims(); // Indexing let top_idx = Tensor::arange_step(0..h as i64, 2, &device); diff --git a/yolox-burn/src/model/boxes.rs b/yolox-burn/src/model/boxes.rs index c459313..3823108 100644 --- a/yolox-burn/src/model/boxes.rs +++ b/yolox-burn/src/model/boxes.rs @@ -32,7 +32,7 @@ pub fn nms( iou_threshold: f32, score_threshold: f32, ) -> Vec>> { - let [batch_size, num_boxes, num_classes] = scores.shape().dims; + let [batch_size, num_boxes, num_classes] = scores.dims(); // Bounding boxes grouped by batch and by (maximum) class index let mut bboxes = boxes diff --git a/yolox-burn/src/model/head.rs b/yolox-burn/src/model/head.rs index d2a0429..6b54a2c 100644 --- a/yolox-burn/src/model/head.rs +++ b/yolox-burn/src/model/head.rs @@ -74,7 +74,7 @@ impl Head { // Output [B, 5 + num_classes, num_anchors] let out = Tensor::cat(vec![reg_out, sigmoid(obj_out), sigmoid(cls_out)], 1); - let [_, _, h, w] = out.shape().dims; + let [_, _, h, w] = out.dims(); (out.flatten(2, 3), (h, w)) }, ) @@ -89,7 +89,7 @@ impl Head { /// Decode bounding box absolute values from regression output offsets. fn decode(&self, outputs: Tensor, shapes: &[(usize, usize)]) -> Tensor { let device = outputs.device(); - let [b, num_anchors, num_outputs] = outputs.shape().dims; + let [b, num_anchors, num_outputs] = outputs.dims(); let (grids, strides) = shapes .iter() diff --git a/yolox-burn/src/model/pafpn.rs b/yolox-burn/src/model/pafpn.rs index 573a652..a6ce38c 100644 --- a/yolox-burn/src/model/pafpn.rs +++ b/yolox-burn/src/model/pafpn.rs @@ -36,7 +36,7 @@ pub struct Pafpn { impl Pafpn { pub fn forward(&self, x: Tensor) -> FpnFeatures { fn upsample(x_in: Tensor, scale: usize) -> Tensor { - let [_, _, h, w] = x_in.shape().dims; + let [_, _, h, w] = x_in.dims(); interpolate( x_in, [h * scale, w * scale], From 5637aaf6081cd76c23f874ae2f298be77585ae5b Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Mon, 11 Mar 2024 08:05:27 -0400 Subject: [PATCH 11/21] Use the new tensor.permute() --- yolox-burn/examples/inference.rs | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/yolox-burn/examples/inference.rs b/yolox-burn/examples/inference.rs index 07c466d..b610c6e 100644 --- a/yolox-burn/examples/inference.rs +++ b/yolox-burn/examples/inference.rs @@ -17,9 +17,8 @@ fn to_tensor( device: &Device, ) -> Tensor { Tensor::::from_data(Data::new(data, Shape::new(shape)).convert(), device) - // permute(2, 0, 1) - .swap_dims(2, 1) // [H, C, W] - .swap_dims(1, 0) // [C, H, W] + // [H, W, C] -> [C, H, W] + .permute([2, 0, 1]) } /// Draws bounding boxes on the given image. From 8daecb9a7f8f4c3d4c4261752bc83e082fd2d83e Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Tue, 12 Mar 2024 12:58:34 -0400 Subject: [PATCH 12/21] Fix comments - Pre-trained weights are from COCO (README) - Remove training outputs TODO - Current example uses YOLOX-Tiny (Nano WIP) --- yolox-burn/README.md | 4 ++-- yolox-burn/examples/inference.rs | 2 +- yolox-burn/src/model/head.rs | 4 ---- 3 files changed, 3 insertions(+), 7 deletions(-) diff --git a/yolox-burn/README.md b/yolox-burn/README.md index 45a2f10..3ca556a 100644 --- a/yolox-burn/README.md +++ b/yolox-burn/README.md @@ -22,7 +22,7 @@ Add this to your `Cargo.toml`: yolox-burn = { git = "https://github.com/burn-rs/models", package = "yolox-burn", default-features = false } ``` -If you want to get the pre-trained ImageNet weights, enable the `pretrained` feature flag. +If you want to get the COCO pre-trained weights, enable the `pretrained` feature flag. ```toml [dependencies] @@ -33,7 +33,7 @@ yolox-burn = { git = "https://github.com/burn-rs/models", package = "yolox-burn" ### Example Usage -The [inference example](examples/inference.rs) initializes a YOLOX-Nano from the COCO +The [inference example](examples/inference.rs) initializes a YOLOX-Tiny from the COCO [pre-trained weights](https://github.com/Megvii-BaseDetection/YOLOX?tab=readme-ov-file#standard-models) with the `NdArray` backend and performs inference on the provided input image. diff --git a/yolox-burn/examples/inference.rs b/yolox-burn/examples/inference.rs index b610c6e..50e6250 100644 --- a/yolox-burn/examples/inference.rs +++ b/yolox-burn/examples/inference.rs @@ -94,7 +94,7 @@ pub fn main() { // Parse arguments let img_path = std::env::args().nth(1).expect("No image path provided"); - // Create YOLOX-Nano + // Create YOLOX-Tiny let device = Default::default(); let model: Yolox = Yolox::yolox_tiny_pretrained(weights::YoloxTiny::Coco, &device) .map_err(|err| format!("Failed to load pre-trained weights.\nError: {err}")) diff --git a/yolox-burn/src/model/head.rs b/yolox-burn/src/model/head.rs index 6b54a2c..04cb52e 100644 --- a/yolox-burn/src/model/head.rs +++ b/yolox-burn/src/model/head.rs @@ -121,10 +121,6 @@ impl Head { 2, ) } - - // TODO - // Training outputs - // fn forward_train(&self, x: FpnFeatures) {} } /// [YOLOX head](Head) configuration. From 1cb8f5ce2f64503f125aaa3738b05984bb3a155d Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Tue, 19 Mar 2024 10:58:37 -0400 Subject: [PATCH 13/21] Add YOLOX-Nano w/ depthwise separable conv (enum) --- yolox-burn/Cargo.toml | 10 +-- yolox-burn/README.md | 2 +- yolox-burn/src/model/blocks.rs | 99 +++++++++++++++++++++++--- yolox-burn/src/model/bottleneck.rs | 13 ++-- yolox-burn/src/model/boxes.rs | 110 +++++++++++++++++++++++++++++ yolox-burn/src/model/darknet.rs | 54 ++++++++++---- yolox-burn/src/model/head.rs | 8 ++- yolox-burn/src/model/pafpn.rs | 58 ++++++++++----- yolox-burn/src/model/weights.rs | 17 ++++- yolox-burn/src/model/yolox.rs | 81 +++++++++++++++++---- 10 files changed, 386 insertions(+), 66 deletions(-) diff --git a/yolox-burn/Cargo.toml b/yolox-burn/Cargo.toml index 012d0f6..5861775 100644 --- a/yolox-burn/Cargo.toml +++ b/yolox-burn/Cargo.toml @@ -12,9 +12,11 @@ pretrained = ["burn/network", "std", "dep:dirs"] [dependencies] # Note: default-features = false is needed to disable std -burn = { git = "https://github.com/tracel-ai/burn.git", default-features = false } -burn-import = { git = "https://github.com/tracel-ai/burn.git" } -itertools = { version = "0.12.1", default-features = false, features = ["use_alloc"]} +burn = { git = "https://github.com/tracel-ai/burn.git", default-features = false, rev = "0138e16af6a85c746035632b2a2df0df8098ebc2" } +burn-import = { git = "https://github.com/tracel-ai/burn.git", rev = "0138e16af6a85c746035632b2a2df0df8098ebc2" } +itertools = { version = "0.12.1", default-features = false, features = [ + "use_alloc", +] } dirs = { version = "5.0.1", optional = true } serde = { version = "1.0.192", default-features = false, features = [ "derive", @@ -24,5 +26,5 @@ serde = { version = "1.0.192", default-features = false, features = [ [dev-dependencies] burn = { git = "https://github.com/tracel-ai/burn.git", features = [ "ndarray", -] } +], rev = "0138e16af6a85c746035632b2a2df0df8098ebc2" } image = { version = "0.24.7", features = ["png", "jpeg"] } diff --git a/yolox-burn/README.md b/yolox-burn/README.md index 3ca556a..175f353 100644 --- a/yolox-burn/README.md +++ b/yolox-burn/README.md @@ -40,5 +40,5 @@ with the `NdArray` backend and performs inference on the provided input image. You can run the example with the following command: ```sh -cargo run --release --features pretrained --example inference samples/dog.jpg +cargo run --release --features pretrained --example inference samples/dog_bike_man.jpg ``` diff --git a/yolox-burn/src/model/blocks.rs b/yolox-burn/src/model/blocks.rs index 3832377..9cdaec9 100644 --- a/yolox-burn/src/model/blocks.rs +++ b/yolox-burn/src/model/blocks.rs @@ -1,5 +1,6 @@ use alloc::vec; use burn::{ + config::Config, module::Module, nn::{ conv::{Conv2d, Conv2dConfig}, @@ -13,6 +14,87 @@ pub fn expand(num_channels: usize, factor: f64) -> usize { (num_channels as f64 * factor).floor() as usize } +/// A base convolution block. +/// Allows to switch between regular and depthwise separable convolution blocks based on the +/// architecture. +#[derive(Module, Debug)] +pub enum Conv { + BaseConv(BaseConv), + DwsConv(DwsConv), +} + +impl Conv { + pub fn forward(&self, x: Tensor) -> Tensor { + match self { + Self::BaseConv(conv) => conv.forward(x), + Self::DwsConv(conv) => conv.forward(x), + } + } +} + +#[derive(Config)] +pub struct ConvConfig { + in_channels: usize, + out_channels: usize, + kernel_size: usize, + stride: usize, + depthwise: bool, +} + +impl ConvConfig { + /// Initialize a new [convolution block](Conv) module. + pub fn init(&self, device: &Device) -> Conv { + if self.depthwise { + Conv::DwsConv( + DwsConvConfig::new( + self.in_channels, + self.out_channels, + self.kernel_size, + self.stride, + ) + .init(device), + ) + } else { + Conv::BaseConv( + BaseConvConfig::new( + self.in_channels, + self.out_channels, + self.kernel_size, + self.stride, + 1, + ) + .init(device), + ) + } + } + + /// Initialize a new [convolution block](Conv) module with a [record](ConvRecord). + pub fn init_with(&self, record: ConvRecord) -> Conv { + match record { + ConvRecord::DwsConv(rec) if self.depthwise => Conv::DwsConv( + DwsConvConfig::new( + self.in_channels, + self.out_channels, + self.kernel_size, + self.stride, + ) + .init_with(rec), + ), + ConvRecord::BaseConv(rec) if !self.depthwise => Conv::BaseConv( + BaseConvConfig::new( + self.in_channels, + self.out_channels, + self.kernel_size, + self.stride, + 1, + ) + .init_with(rec), + ), + _ => panic!("Invalid record for depthwise={}", self.depthwise), + } + } +} + /// A Conv2d -> BatchNorm -> activation block. #[derive(Module, Debug)] pub struct BaseConv { @@ -77,7 +159,8 @@ impl BaseConvConfig { } } -/// A [depthwise separable convolution](https://paperswithcode.com/method/depthwise-separable-convolution) block. +/// A [depthwise separable convolution](https://paperswithcode.com/method/depthwise-separable-convolution) +/// block. Both depthwise and pointwise blocks consist of a Conv2d -> BatchNorm -> activation block. #[derive(Module, Debug)] pub struct DwsConv { dconv: BaseConv, @@ -200,8 +283,8 @@ impl FocusConfig { /// Dual convolution block used for feature extraction in the prediction head. #[derive(Module, Debug)] pub struct ConvBlock { - conv0: BaseConv, - conv1: BaseConv, + conv0: Conv, + conv1: Conv, } impl ConvBlock { @@ -213,15 +296,15 @@ impl ConvBlock { /// [Dual convolution block](ConvBlock) configuration. pub struct ConvBlockConfig { - conv0: BaseConvConfig, - conv1: BaseConvConfig, + conv0: ConvConfig, + conv1: ConvConfig, } impl ConvBlockConfig { /// Create a new instance of the dual convolution block [config](ConvBlockConfig). - pub fn new(channels: usize, kernel_size: usize, stride: usize) -> Self { - let conv0 = BaseConvConfig::new(channels, channels, kernel_size, stride, 1); - let conv1 = BaseConvConfig::new(channels, channels, kernel_size, stride, 1); + pub fn new(channels: usize, kernel_size: usize, stride: usize, depthwise: bool) -> Self { + let conv0 = ConvConfig::new(channels, channels, kernel_size, stride, depthwise); + let conv1 = ConvConfig::new(channels, channels, kernel_size, stride, depthwise); Self { conv0, conv1 } } diff --git a/yolox-burn/src/model/bottleneck.rs b/yolox-burn/src/model/bottleneck.rs index d83f0a6..4a7df3e 100644 --- a/yolox-burn/src/model/bottleneck.rs +++ b/yolox-burn/src/model/bottleneck.rs @@ -5,13 +5,13 @@ use burn::{ tensor::{backend::Backend, Device, Tensor}, }; -use super::blocks::{expand, BaseConv, BaseConvConfig}; +use super::blocks::{expand, BaseConv, BaseConvConfig, Conv, ConvConfig}; /// Standard bottleneck block. #[derive(Module, Debug)] pub struct Bottleneck { conv1: BaseConv, - conv2: BaseConv, + conv2: Conv, shortcut: bool, } @@ -33,18 +33,18 @@ impl Bottleneck { /// [Bottleneck block](Bottleneck) configuration. struct BottleneckConfig { conv1: BaseConvConfig, - conv2: BaseConvConfig, + conv2: ConvConfig, shortcut: bool, } impl BottleneckConfig { /// Create a new instance of the bottleneck block [config](BottleneckConfig). - pub fn new(in_channels: usize, out_channels: usize, shortcut: bool) -> Self { + pub fn new(in_channels: usize, out_channels: usize, shortcut: bool, depthwise: bool) -> Self { // In practice, expansion = 1.0 and no shortcut connection is used let hidden_channels = out_channels; let conv1 = BaseConvConfig::new(in_channels, hidden_channels, 1, 1, 1); - let conv2 = BaseConvConfig::new(hidden_channels, out_channels, 3, 1, 1); + let conv2 = ConvConfig::new(hidden_channels, out_channels, 3, 1, depthwise); Self { conv1, @@ -186,6 +186,7 @@ impl CspBottleneckConfig { num_blocks: usize, expansion: f64, shortcut: bool, + depthwise: bool, ) -> Self { assert!( expansion > 0.0 && expansion <= 1.0, @@ -198,7 +199,7 @@ impl CspBottleneckConfig { let conv2 = BaseConvConfig::new(in_channels, hidden_channels, 1, 1, 1); let conv3 = BaseConvConfig::new(2 * hidden_channels, out_channels, 1, 1, 1); let m = (0..num_blocks) - .map(|_| BottleneckConfig::new(hidden_channels, hidden_channels, shortcut)) + .map(|_| BottleneckConfig::new(hidden_channels, hidden_channels, shortcut, depthwise)) .collect(); Self { diff --git a/yolox-burn/src/model/boxes.rs b/yolox-burn/src/model/boxes.rs index 3823108..8fb953e 100644 --- a/yolox-burn/src/model/boxes.rs +++ b/yolox-burn/src/model/boxes.rs @@ -26,6 +26,9 @@ pub struct BoundingBox { /// /// Vector of bounding boxes grouped by class for each batch. The boxes are sorted in decreasing /// order of scores for each class. +/// +/// Selected indices of the boxes kept by NMS, sorted in decreasing order of scores. +/// The selected index format is `[batch_index, class_index, box_index]`. pub fn nms( boxes: Tensor, scores: Tensor, @@ -136,3 +139,110 @@ pub fn non_maximum_suppression(bboxes: &mut [Vec], threshold: f32) bboxes_for_class.truncate(current_index); } } + +// pub fn nms( +// boxes: Tensor, +// scores: Tensor, +// iou_threshold: f64, +// score_threshold: f64, +// ) -> () { +// // Tensor +// let [_, num_boxes, num_classes] = scores.shape().dims; +// let device = boxes.device(); +// let is_valid = Tensor::::from_data([true; 1], &device); +// // let values: Tensor = Tensor::from_data([1, 0, 3, 4, -1], &device); + +// let x = Tensor::arange(0..6 as i64, &device).reshape(Shape::new([2, 3])); +// let mask = x.greater_elem(1); + +// let selected_indices = boxes +// .iter_dim(0) +// .enumerate() +// // Per-batch NMS +// .map(|(batch_idx, candidate_boxes)| { +// // Per-class filtering +// (0..num_classes).map(|cls_idx| { +// scores.select( +// 0, +// Tensor::::full(Shape::new([1]), batch_idx as i64, &device), +// ); +// // [batch_size, num_boxes, 1] +// let scores_mask = scores.greater_equal_elem(score_threshold).any_dim(2); +// // 1. `non_zero` to get the mask's valid boxes indices +// // 2. `select` to return only valid boxes (by score threshold) +// // 3. `argsort` to get the indices ranked by classification score +// // NOTE: we could do it all on CPU (preds to_vec) to iterate and compare as done in +// // [candle](https://github.com/huggingface/candle/blob/main/candle-examples/examples/yolo-v3/main.rs#L38) +// // ([nms](https://github.com/huggingface/candle/blob/main/candle-transformers/src/object_detection.rs#L31)) +// // but this is not so nice +// let box_idx = scores_mask +// .iter_dim(1) +// .filter_map(|box_idx| { +// // NOTE: bool tensors do not support `select` and `into_scalar()` +// if scores_mask +// .slice([batch_idx..batch_idx + 1]) +// .any() +// .int() +// .into_scalar() as bool +// { +// Some(box_idx) +// } else { +// None +// } +// }) +// .collect(); +// // scores[batch_idx, ] +// // [batch_size, num_boxes, 1] +// let scores_max = scores.gather(dim, indices); +// }) +// }); +// } + +// std::vector selected_indices; +// std::vector selected_boxes_inside_class; +// selected_boxes_inside_class.reserve(std::min(static_cast(max_output_boxes_per_class), pc.num_boxes_)); + +// for (int64_t batch_index = 0; batch_index < pc.num_batches_; ++batch_index) { +// for (int64_t class_index = 0; class_index < pc.num_classes_; ++class_index) { +// int64_t box_score_offset = (batch_index * pc.num_classes_ + class_index) * pc.num_boxes_; +// const float* batch_boxes = boxes_data + (batch_index * pc.num_boxes_ * 4); +// std::vector candidate_boxes; +// candidate_boxes.reserve(pc.num_boxes_); + +// // Filter by score_threshold_ +// const auto* class_scores = scores_data + box_score_offset; +// if (pc.score_threshold_ != nullptr) { +// for (int64_t box_index = 0; box_index < pc.num_boxes_; ++box_index, ++class_scores) { +// if (*class_scores > score_threshold) { +// candidate_boxes.emplace_back(*class_scores, box_index); +// } +// } +// } else { +// for (int64_t box_index = 0; box_index < pc.num_boxes_; ++box_index, ++class_scores) { +// candidate_boxes.emplace_back(*class_scores, box_index); +// } +// } +// std::priority_queue> sorted_boxes(std::less(), std::move(candidate_boxes)); + +// selected_boxes_inside_class.clear(); +// // Get the next box with top score, filter by iou_threshold +// while (!sorted_boxes.empty() && static_cast(selected_boxes_inside_class.size()) < max_output_boxes_per_class) { +// const BoxInfoPtr& next_top_score = sorted_boxes.top(); + +// bool selected = true; +// // Check with existing selected boxes for this class, suppress if exceed the IOU (Intersection Over Union) threshold +// for (const auto& selected_index : selected_boxes_inside_class) { +// if (SuppressByIOU(batch_boxes, next_top_score.index_, selected_index.index_, center_point_box, iou_threshold)) { +// selected = false; +// break; +// } +// } + +// if (selected) { +// selected_boxes_inside_class.push_back(next_top_score); +// selected_indices.emplace_back(batch_index, class_index, next_top_score.index_); +// } +// sorted_boxes.pop(); +// } // while +// } // for class_index +// } // for batch_index diff --git a/yolox-burn/src/model/darknet.rs b/yolox-burn/src/model/darknet.rs index cc6b84a..b17b4b6 100644 --- a/yolox-burn/src/model/darknet.rs +++ b/yolox-burn/src/model/darknet.rs @@ -3,7 +3,7 @@ use core::cmp::max; use crate::model::blocks::expand; use super::{ - blocks::{BaseConv, BaseConvConfig, Focus, FocusConfig}, + blocks::{Conv, ConvConfig, Focus, FocusConfig}, bottleneck::{CspBottleneck, CspBottleneckConfig, SppBottleneck, SppBottleneckConfig}, }; use burn::{ @@ -47,7 +47,7 @@ pub struct CspDarknetConfig { impl CspDarknetConfig { /// Create a new instance of the CSPDarknet-53 [config](CspDarknetConfig). - pub fn new(depth: f64, width: f64) -> Self { + pub fn new(depth: f64, width: f64, depthwise: bool) -> Self { assert!( [0.33, 0.67, 1.0, 1.33].contains(&depth), "invalid depth value {depth}" @@ -62,12 +62,34 @@ impl CspDarknetConfig { let base_depth = max((depth * 3_f64).round() as usize, 1); let stem = FocusConfig::new(3, base_channels, 3, 1); - let dark2 = CspBlockConfig::new(base_channels, base_channels * 2, base_depth, false); - let dark3 = - CspBlockConfig::new(base_channels * 2, base_channels * 4, base_depth * 3, false); - let dark4 = - CspBlockConfig::new(base_channels * 4, base_channels * 8, base_depth * 3, false); - let dark5 = CspBlockConfig::new(base_channels * 8, base_channels * 16, base_depth, true); + let dark2 = CspBlockConfig::new( + base_channels, + base_channels * 2, + base_depth, + false, + depthwise, + ); + let dark3 = CspBlockConfig::new( + base_channels * 2, + base_channels * 4, + base_depth * 3, + false, + depthwise, + ); + let dark4 = CspBlockConfig::new( + base_channels * 4, + base_channels * 8, + base_depth * 3, + false, + depthwise, + ); + let dark5 = CspBlockConfig::new( + base_channels * 8, + base_channels * 16, + base_depth, + true, + depthwise, + ); Self { stem, @@ -105,7 +127,7 @@ impl CspDarknetConfig { /// The SppBottleneck layer is only used in the last block of [CSPDarknet-53](CspDarknet). #[derive(Module, Debug)] pub struct CspBlock { - conv: BaseConv, + conv: Conv, c3: CspBottleneck, spp: Option>, } @@ -124,16 +146,22 @@ impl CspBlock { /// [CSP block](CspBlock) configuration. pub struct CspBlockConfig { - conv: BaseConvConfig, + conv: ConvConfig, c3: CspBottleneckConfig, spp: Option, } impl CspBlockConfig { /// Create a new instance of the CSP block [config](CspBlockConfig). - pub fn new(in_channels: usize, out_channels: usize, depth: usize, spp: bool) -> Self { - let conv = BaseConvConfig::new(in_channels, out_channels, 3, 2, 1); - let c3 = CspBottleneckConfig::new(out_channels, out_channels, depth, 0.5, !spp); + pub fn new( + in_channels: usize, + out_channels: usize, + depth: usize, + spp: bool, + depthwise: bool, + ) -> Self { + let conv = ConvConfig::new(in_channels, out_channels, 3, 2, depthwise); + let c3 = CspBottleneckConfig::new(out_channels, out_channels, depth, 0.5, !spp, depthwise); let spp = if spp { Some(SppBottleneckConfig::new(out_channels, out_channels)) diff --git a/yolox-burn/src/model/head.rs b/yolox-burn/src/model/head.rs index 04cb52e..ef60bc6 100644 --- a/yolox-burn/src/model/head.rs +++ b/yolox-burn/src/model/head.rs @@ -135,7 +135,7 @@ pub struct HeadConfig { impl HeadConfig { /// Create a new instance of the YOLOX head [config](HeadConfig). - pub fn new(num_classes: usize, width: f64) -> Self { + pub fn new(num_classes: usize, width: f64, depthwise: bool) -> Self { let hidden_channels: usize = 256; // Initialize conv2d biases for classification and objectness heads let bias = -f64::ln((1.0 - PRIOR_PROB) / PRIOR_PROB); @@ -150,8 +150,10 @@ impl HeadConfig { 1, ); - let cls_conv = ConvBlockConfig::new(expand(hidden_channels, width), 3, 1); - let reg_conv = ConvBlockConfig::new(expand(hidden_channels, width), 3, 1); + let cls_conv = + ConvBlockConfig::new(expand(hidden_channels, width), 3, 1, depthwise); + let reg_conv = + ConvBlockConfig::new(expand(hidden_channels, width), 3, 1, depthwise); let cls_pred = Conv2dConfig::new([expand(hidden_channels, width), num_classes], [1, 1]) diff --git a/yolox-burn/src/model/pafpn.rs b/yolox-burn/src/model/pafpn.rs index a6ce38c..4d27a91 100644 --- a/yolox-burn/src/model/pafpn.rs +++ b/yolox-burn/src/model/pafpn.rs @@ -10,7 +10,7 @@ use burn::{ }; use super::{ - blocks::{expand, BaseConv, BaseConvConfig}, + blocks::{expand, BaseConv, BaseConvConfig, Conv, ConvConfig}, bottleneck::{CspBottleneck, CspBottleneckConfig}, darknet::{CspDarknet, CspDarknetConfig}, }; @@ -29,8 +29,8 @@ pub struct Pafpn { c3_p3: CspBottleneck, c3_p4: CspBottleneck, reduce_conv1: BaseConv, - bu_conv1: BaseConv, // bottom-up conv - bu_conv2: BaseConv, // bottom-up conv + bu_conv1: Conv, // bottom-up conv + bu_conv2: Conv, // bottom-up conv } impl Pafpn { @@ -78,13 +78,13 @@ pub struct PafpnConfig { c3_p3: CspBottleneckConfig, c3_p4: CspBottleneckConfig, reduce_conv1: BaseConvConfig, - bu_conv1: BaseConvConfig, // bottom-up conv - bu_conv2: BaseConvConfig, // bottom-up conv + bu_conv1: ConvConfig, // bottom-up conv + bu_conv2: ConvConfig, // bottom-up conv } impl PafpnConfig { /// Create a new instance of the PAFPN [config](PafpnConfig). - pub fn new(depth: f64, width: f64) -> Self { + pub fn new(depth: f64, width: f64, depthwise: bool) -> Self { assert!( [0.33, 0.67, 1.0, 1.33].contains(&depth), "invalid depth value {depth}" @@ -106,22 +106,46 @@ impl PafpnConfig { ]; let num_blocks = (3_f64 * depth).round() as usize; - let backbone = CspDarknetConfig::new(depth, width); + let backbone = CspDarknetConfig::new(depth, width, depthwise); let lateral_conv0 = BaseConvConfig::new(in_channels[2], in_channels[1], 1, 1, 1); - let c3_p4 = - CspBottleneckConfig::new(hidden_channels[1], in_channels[1], num_blocks, 0.5, false); + let c3_p4 = CspBottleneckConfig::new( + hidden_channels[1], + in_channels[1], + num_blocks, + 0.5, + false, + depthwise, + ); let reduce_conv1 = BaseConvConfig::new(in_channels[1], in_channels[0], 1, 1, 1); - let c3_p3 = - CspBottleneckConfig::new(hidden_channels[0], in_channels[0], num_blocks, 0.5, false); + let c3_p3 = CspBottleneckConfig::new( + hidden_channels[0], + in_channels[0], + num_blocks, + 0.5, + false, + depthwise, + ); - let bu_conv2 = BaseConvConfig::new(in_channels[0], in_channels[0], 3, 2, 1); - let c3_n3 = - CspBottleneckConfig::new(hidden_channels[0], in_channels[1], num_blocks, 0.5, false); + let bu_conv2 = ConvConfig::new(in_channels[0], in_channels[0], 3, 2, depthwise); + let c3_n3 = CspBottleneckConfig::new( + hidden_channels[0], + in_channels[1], + num_blocks, + 0.5, + false, + depthwise, + ); - let bu_conv1 = BaseConvConfig::new(in_channels[1], in_channels[1], 3, 2, 1); - let c3_n4 = - CspBottleneckConfig::new(hidden_channels[1], in_channels[2], num_blocks, 0.5, false); + let bu_conv1 = ConvConfig::new(in_channels[1], in_channels[1], 3, 2, depthwise); + let c3_n4 = CspBottleneckConfig::new( + hidden_channels[1], + in_channels[2], + num_blocks, + 0.5, + false, + depthwise, + ); Self { backbone, diff --git a/yolox-burn/src/model/weights.rs b/yolox-burn/src/model/weights.rs index 8de36b1..6d5f268 100644 --- a/yolox-burn/src/model/weights.rs +++ b/yolox-burn/src/model/weights.rs @@ -52,7 +52,22 @@ pub trait WeightsMeta { fn weights(&self) -> Weights; } -/// YOLOX-S pre-trained weights. +/// YOLOX-Nano pre-trained weights. +pub enum YoloxNano { + /// These weights were released after the original paper implementation with slightly better results. + /// mAP (val2017): 25.8 + Coco, +} +impl WeightsMeta for YoloxNano { + fn weights(&self) -> Weights { + Weights { + url: "https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_nano.pth", + num_classes: 80, + } + } +} + +/// YOLOX-Tiny pre-trained weights. pub enum YoloxTiny { /// These weights were released after the original paper implementation with slightly better results. /// mAP (val2017): 32.8 diff --git a/yolox-burn/src/model/yolox.rs b/yolox-burn/src/model/yolox.rs index 30e606b..e9a6a3f 100644 --- a/yolox-burn/src/model/yolox.rs +++ b/yolox-burn/src/model/yolox.rs @@ -31,6 +31,44 @@ impl Yolox { self.head.forward(features) } + /// YOLOX-Nano from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430). + /// + /// # Arguments + /// + /// * `num_classes`: Number of output classes of the model. + /// * `device` - Device to create the module on. + /// + /// # Returns + /// + /// A YOLOX-Nano module. + pub fn yolox_nano(num_classes: usize, device: &Device) -> Self { + YoloxConfig::new(0.33, 0.25, num_classes, true).init(device) + } + + /// YOLOX-Nano from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430) + /// with pre-trained weights. + /// + /// # Arguments + /// + /// * `num_classes`: Number of output classes of the model. + /// * `device` - Device to create the module on. + /// + /// # Returns + /// + /// A YOLOX-Nano module with pre-trained weights. + #[cfg(feature = "pretrained")] + pub fn yolox_nano_pretrained( + weights: weights::YoloxNano, + device: &Device, + ) -> Result { + let weights = weights.weights(); + let record = Self::load_weights_record(&weights, device)?; + + let model = YoloxConfig::new(0.33, 0.25, weights.num_classes, true).init_with(record); + + Ok(model) + } + /// YOLOX-Tiny from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430). /// /// # Arguments @@ -42,7 +80,7 @@ impl Yolox { /// /// A YOLOX-Tiny module. pub fn yolox_tiny(num_classes: usize, device: &Device) -> Self { - YoloxConfig::new(0.33, 0.375, num_classes).init(device) + YoloxConfig::new(0.33, 0.375, num_classes, false).init(device) } /// YOLOX-Tiny from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430) @@ -64,7 +102,7 @@ impl Yolox { let weights = weights.weights(); let record = Self::load_weights_record(&weights, device)?; - let model = YoloxConfig::new(0.33, 0.375, weights.num_classes).init_with(record); + let model = YoloxConfig::new(0.33, 0.375, weights.num_classes, false).init_with(record); Ok(model) } @@ -80,7 +118,7 @@ impl Yolox { /// /// A YOLOX-S module. pub fn yolox_s(num_classes: usize, device: &Device) -> Self { - YoloxConfig::new(0.33, 0.50, num_classes).init(device) + YoloxConfig::new(0.33, 0.50, num_classes, false).init(device) } /// YOLOX-S from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430) @@ -102,7 +140,7 @@ impl Yolox { let weights = weights.weights(); let record = Self::load_weights_record(&weights, device)?; - let model = YoloxConfig::new(0.33, 0.50, weights.num_classes).init_with(record); + let model = YoloxConfig::new(0.33, 0.50, weights.num_classes, false).init_with(record); Ok(model) } @@ -118,7 +156,7 @@ impl Yolox { /// /// A YOLOX-M module. pub fn yolox_m(num_classes: usize, device: &Device) -> Self { - YoloxConfig::new(0.67, 0.75, num_classes).init(device) + YoloxConfig::new(0.67, 0.75, num_classes, false).init(device) } /// YOLOX-M from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430) @@ -140,7 +178,7 @@ impl Yolox { let weights = weights.weights(); let record = Self::load_weights_record(&weights, device)?; - let model = YoloxConfig::new(0.67, 0.75, weights.num_classes).init_with(record); + let model = YoloxConfig::new(0.67, 0.75, weights.num_classes, false).init_with(record); Ok(model) } @@ -156,7 +194,7 @@ impl Yolox { /// /// A YOLOX-L module. pub fn yolox_l(num_classes: usize, device: &Device) -> Self { - YoloxConfig::new(1., 1., num_classes).init(device) + YoloxConfig::new(1., 1., num_classes, false).init(device) } /// YOLOX-L from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430) @@ -178,7 +216,7 @@ impl Yolox { let weights = weights.weights(); let record = Self::load_weights_record(&weights, device)?; - let model = YoloxConfig::new(1., 1., weights.num_classes).init_with(record); + let model = YoloxConfig::new(1., 1., weights.num_classes, false).init_with(record); Ok(model) } @@ -194,7 +232,7 @@ impl Yolox { /// /// A YOLOX-X module. pub fn yolox_x(num_classes: usize, device: &Device) -> Self { - YoloxConfig::new(1.33, 1.25, num_classes).init(device) + YoloxConfig::new(1.33, 1.25, num_classes, false).init(device) } /// YOLOX-X from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430) @@ -216,7 +254,7 @@ impl Yolox { let weights = weights.weights(); let record = Self::load_weights_record(&weights, device)?; - let model = YoloxConfig::new(1.33, 1.25, weights.num_classes).init_with(record); + let model = YoloxConfig::new(1.33, 1.25, weights.num_classes, false).init_with(record); Ok(model) } @@ -250,6 +288,23 @@ impl Yolox { "(head\\.(cls|reg)_convs\\.[0-9]+)\\.([0-9]+)\\.(.+)", "$1.conv$3.$4", ); + + // if depthwise { + // load_args = load_args + // // Map backbone.backbone.dark[i].0.{dconv | pconv}.* -> backbone.backbone.dark[i].conv.* + // .with_key_remap( + // "(backbone\\.backbone\\.dark[2-5])\\.0\\.([dp]conv\\.+)", + // "$1.conv.$2", + // ) + // // Map backbone.backbone.dark[i].1.* -> backbone.backbone.dark[i].c3.* + // .with_key_remap("(backbone\\.backbone\\.dark[2-4])\\.1\\.(.+)", "$1.c3.$2") + // } else { + // load_args = load_args + // // Map backbone.backbone.dark[i].0.* -> backbone.backbone.dark[i].conv.* + // .with_key_remap("(backbone\\.backbone\\.dark[2-5])\\.0\\.(.+)", "$1.conv.$2") + // // Map backbone.backbone.dark[i].1.* -> backbone.backbone.dark[i].c3.* + // .with_key_remap("(backbone\\.backbone\\.dark[2-4])\\.1\\.(.+)", "$1.c3.$2") + // } let record = PyTorchFileRecorder::::new().load(load_args, device)?; Ok(record) @@ -264,9 +319,9 @@ pub struct YoloxConfig { impl YoloxConfig { /// Create a new instance of the YOLOX detector [config](YoloxConfig). - pub fn new(depth: f64, width: f64, num_classes: usize) -> Self { - let backbone = PafpnConfig::new(depth, width); - let head = HeadConfig::new(num_classes, width); + pub fn new(depth: f64, width: f64, num_classes: usize, depthwise: bool) -> Self { + let backbone = PafpnConfig::new(depth, width, depthwise); + let head = HeadConfig::new(num_classes, width, depthwise); Self { backbone, head } } From 7bff1e5e5129db9a4e9ed8bd59ce6b1671a2b6f3 Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Tue, 19 Mar 2024 13:31:25 -0400 Subject: [PATCH 14/21] Remove dead code comment --- yolox-burn/src/model/boxes.rs | 107 ---------------------------------- 1 file changed, 107 deletions(-) diff --git a/yolox-burn/src/model/boxes.rs b/yolox-burn/src/model/boxes.rs index 8fb953e..f3ddf9d 100644 --- a/yolox-burn/src/model/boxes.rs +++ b/yolox-burn/src/model/boxes.rs @@ -139,110 +139,3 @@ pub fn non_maximum_suppression(bboxes: &mut [Vec], threshold: f32) bboxes_for_class.truncate(current_index); } } - -// pub fn nms( -// boxes: Tensor, -// scores: Tensor, -// iou_threshold: f64, -// score_threshold: f64, -// ) -> () { -// // Tensor -// let [_, num_boxes, num_classes] = scores.shape().dims; -// let device = boxes.device(); -// let is_valid = Tensor::::from_data([true; 1], &device); -// // let values: Tensor = Tensor::from_data([1, 0, 3, 4, -1], &device); - -// let x = Tensor::arange(0..6 as i64, &device).reshape(Shape::new([2, 3])); -// let mask = x.greater_elem(1); - -// let selected_indices = boxes -// .iter_dim(0) -// .enumerate() -// // Per-batch NMS -// .map(|(batch_idx, candidate_boxes)| { -// // Per-class filtering -// (0..num_classes).map(|cls_idx| { -// scores.select( -// 0, -// Tensor::::full(Shape::new([1]), batch_idx as i64, &device), -// ); -// // [batch_size, num_boxes, 1] -// let scores_mask = scores.greater_equal_elem(score_threshold).any_dim(2); -// // 1. `non_zero` to get the mask's valid boxes indices -// // 2. `select` to return only valid boxes (by score threshold) -// // 3. `argsort` to get the indices ranked by classification score -// // NOTE: we could do it all on CPU (preds to_vec) to iterate and compare as done in -// // [candle](https://github.com/huggingface/candle/blob/main/candle-examples/examples/yolo-v3/main.rs#L38) -// // ([nms](https://github.com/huggingface/candle/blob/main/candle-transformers/src/object_detection.rs#L31)) -// // but this is not so nice -// let box_idx = scores_mask -// .iter_dim(1) -// .filter_map(|box_idx| { -// // NOTE: bool tensors do not support `select` and `into_scalar()` -// if scores_mask -// .slice([batch_idx..batch_idx + 1]) -// .any() -// .int() -// .into_scalar() as bool -// { -// Some(box_idx) -// } else { -// None -// } -// }) -// .collect(); -// // scores[batch_idx, ] -// // [batch_size, num_boxes, 1] -// let scores_max = scores.gather(dim, indices); -// }) -// }); -// } - -// std::vector selected_indices; -// std::vector selected_boxes_inside_class; -// selected_boxes_inside_class.reserve(std::min(static_cast(max_output_boxes_per_class), pc.num_boxes_)); - -// for (int64_t batch_index = 0; batch_index < pc.num_batches_; ++batch_index) { -// for (int64_t class_index = 0; class_index < pc.num_classes_; ++class_index) { -// int64_t box_score_offset = (batch_index * pc.num_classes_ + class_index) * pc.num_boxes_; -// const float* batch_boxes = boxes_data + (batch_index * pc.num_boxes_ * 4); -// std::vector candidate_boxes; -// candidate_boxes.reserve(pc.num_boxes_); - -// // Filter by score_threshold_ -// const auto* class_scores = scores_data + box_score_offset; -// if (pc.score_threshold_ != nullptr) { -// for (int64_t box_index = 0; box_index < pc.num_boxes_; ++box_index, ++class_scores) { -// if (*class_scores > score_threshold) { -// candidate_boxes.emplace_back(*class_scores, box_index); -// } -// } -// } else { -// for (int64_t box_index = 0; box_index < pc.num_boxes_; ++box_index, ++class_scores) { -// candidate_boxes.emplace_back(*class_scores, box_index); -// } -// } -// std::priority_queue> sorted_boxes(std::less(), std::move(candidate_boxes)); - -// selected_boxes_inside_class.clear(); -// // Get the next box with top score, filter by iou_threshold -// while (!sorted_boxes.empty() && static_cast(selected_boxes_inside_class.size()) < max_output_boxes_per_class) { -// const BoxInfoPtr& next_top_score = sorted_boxes.top(); - -// bool selected = true; -// // Check with existing selected boxes for this class, suppress if exceed the IOU (Intersection Over Union) threshold -// for (const auto& selected_index : selected_boxes_inside_class) { -// if (SuppressByIOU(batch_boxes, next_top_score.index_, selected_index.index_, center_point_box, iou_threshold)) { -// selected = false; -// break; -// } -// } - -// if (selected) { -// selected_boxes_inside_class.push_back(next_top_score); -// selected_indices.emplace_back(batch_index, class_index, next_top_score.index_); -// } -// sorted_boxes.pop(); -// } // while -// } // for class_index -// } // for batch_index From 8a9b5e21787887946388862475583a457ab48699 Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Tue, 19 Mar 2024 13:32:21 -0400 Subject: [PATCH 15/21] Remove incorrect return comment --- yolox-burn/src/model/boxes.rs | 3 --- 1 file changed, 3 deletions(-) diff --git a/yolox-burn/src/model/boxes.rs b/yolox-burn/src/model/boxes.rs index f3ddf9d..3823108 100644 --- a/yolox-burn/src/model/boxes.rs +++ b/yolox-burn/src/model/boxes.rs @@ -26,9 +26,6 @@ pub struct BoundingBox { /// /// Vector of bounding boxes grouped by class for each batch. The boxes are sorted in decreasing /// order of scores for each class. -/// -/// Selected indices of the boxes kept by NMS, sorted in decreasing order of scores. -/// The selected index format is `[batch_index, class_index, box_index]`. pub fn nms( boxes: Tensor, scores: Tensor, From c0dc0234c18601a1ea699eaa97c05c27ae41e232 Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Wed, 20 Mar 2024 10:48:46 -0400 Subject: [PATCH 16/21] Add YOLOX to models README --- README.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 226ddaf..e8b235c 100644 --- a/README.md +++ b/README.md @@ -5,11 +5,12 @@ examples constructed using the [Burn](https://github.com/burn-rs/burn) deep lear ## Collection of Official Models -| Model | Description | Repository Link | -|------------------------------------------------|-------------------------------------------------------|----------------------------------------------| -| [SqueezeNet](https://arxiv.org/abs/1602.07360) | A small CNN-based model for image classification. | [squeezenet-burn](squeezenet-burn/README.md) | -| [ResNet](https://arxiv.org/abs/1512.03385) | A CNN based on residual blocks with skip connections. | [resnet-burn](resnet-burn/README.md) | -| [RoBERTa](https://arxiv.org/abs/1907.11692) | A robustly optimized BERT pretraining approach. | [bert-burn](bert-burn/README.md) | +| Model | Description | Repository Link | +|------------------------------------------------|----------------------------------------------------------|----------------------------------------------| +| [SqueezeNet](https://arxiv.org/abs/1602.07360) | A small CNN-based model for image classification. | [squeezenet-burn](squeezenet-burn/README.md) | +| [ResNet](https://arxiv.org/abs/1512.03385) | A CNN based on residual blocks with skip connections. | [resnet-burn](resnet-burn/README.md) | +| [RoBERTa](https://arxiv.org/abs/1907.11692) | A robustly optimized BERT pretraining approach. | [bert-burn](bert-burn/README.md) | +| [YOLOX](https://arxiv.org/abs/2107.08430) | A single-stage object detector based on the YOLO series. | [yolox-burn](yolox-burn/README.md) | ## Community Contributions From 10336396ce04d3dbcaaff5f6b2e7d38afc2d807d Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Wed, 20 Mar 2024 12:40:31 -0400 Subject: [PATCH 17/21] Fix dead comments and add enum variants doc --- yolox-burn/src/model/blocks.rs | 3 ++- yolox-burn/src/model/yolox.rs | 19 ------------------- 2 files changed, 2 insertions(+), 20 deletions(-) diff --git a/yolox-burn/src/model/blocks.rs b/yolox-burn/src/model/blocks.rs index 9cdaec9..11372c8 100644 --- a/yolox-burn/src/model/blocks.rs +++ b/yolox-burn/src/model/blocks.rs @@ -19,7 +19,9 @@ pub fn expand(num_channels: usize, factor: f64) -> usize { /// architecture. #[derive(Module, Debug)] pub enum Conv { + /// Basic convolution block, used for all variants except YOLOX-Nano. BaseConv(BaseConv), + /// Depthwise separable convolution block, used by YOLOX-Nano. DwsConv(DwsConv), } @@ -107,7 +109,6 @@ impl BaseConv { let x = self.conv.forward(x); let x = self.bn.forward(x); - // SiLU not in nn yet silu(x) } } diff --git a/yolox-burn/src/model/yolox.rs b/yolox-burn/src/model/yolox.rs index e9a6a3f..73cac2e 100644 --- a/yolox-burn/src/model/yolox.rs +++ b/yolox-burn/src/model/yolox.rs @@ -15,9 +15,6 @@ use { burn_import::pytorch::{LoadArgs, PyTorchFileRecorder}, }; -// TODO: YOLOX -// Only YOLOX-Nano uses depthwise=True (DwsConv) - /// [YOLOX](https://paperswithcode.com/method/yolox) object detection architecture. #[derive(Module, Debug)] pub struct Yolox { @@ -289,22 +286,6 @@ impl Yolox { "$1.conv$3.$4", ); - // if depthwise { - // load_args = load_args - // // Map backbone.backbone.dark[i].0.{dconv | pconv}.* -> backbone.backbone.dark[i].conv.* - // .with_key_remap( - // "(backbone\\.backbone\\.dark[2-5])\\.0\\.([dp]conv\\.+)", - // "$1.conv.$2", - // ) - // // Map backbone.backbone.dark[i].1.* -> backbone.backbone.dark[i].c3.* - // .with_key_remap("(backbone\\.backbone\\.dark[2-4])\\.1\\.(.+)", "$1.c3.$2") - // } else { - // load_args = load_args - // // Map backbone.backbone.dark[i].0.* -> backbone.backbone.dark[i].conv.* - // .with_key_remap("(backbone\\.backbone\\.dark[2-5])\\.0\\.(.+)", "$1.conv.$2") - // // Map backbone.backbone.dark[i].1.* -> backbone.backbone.dark[i].c3.* - // .with_key_remap("(backbone\\.backbone\\.dark[2-4])\\.1\\.(.+)", "$1.c3.$2") - // } let record = PyTorchFileRecorder::::new().load(load_args, device)?; Ok(record) From 6b8f51b3112f14c2504a63ec6aa850a0cac86352 Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Wed, 20 Mar 2024 12:42:35 -0400 Subject: [PATCH 18/21] Rephrase enum variants doc --- yolox-burn/src/model/blocks.rs | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/yolox-burn/src/model/blocks.rs b/yolox-burn/src/model/blocks.rs index 11372c8..3aee6f7 100644 --- a/yolox-burn/src/model/blocks.rs +++ b/yolox-burn/src/model/blocks.rs @@ -19,9 +19,9 @@ pub fn expand(num_channels: usize, factor: f64) -> usize { /// architecture. #[derive(Module, Debug)] pub enum Conv { - /// Basic convolution block, used for all variants except YOLOX-Nano. + /// Basic convolution block used for all variants. BaseConv(BaseConv), - /// Depthwise separable convolution block, used by YOLOX-Nano. + /// Depthwise separable convolution block, used for some blocks by YOLOX-Nano. DwsConv(DwsConv), } From 13654d78ace70c8b5cdc6952b1fe884071b856f9 Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Wed, 3 Apr 2024 15:00:58 -0400 Subject: [PATCH 19/21] Change burn-rs -> tracel-ai links --- yolox-burn/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/yolox-burn/README.md b/yolox-burn/README.md index 175f353..3aea944 100644 --- a/yolox-burn/README.md +++ b/yolox-burn/README.md @@ -19,14 +19,14 @@ Add this to your `Cargo.toml`: ```toml [dependencies] -yolox-burn = { git = "https://github.com/burn-rs/models", package = "yolox-burn", default-features = false } +yolox-burn = { git = "https://github.com/tracel-ai/models", package = "yolox-burn", default-features = false } ``` If you want to get the COCO pre-trained weights, enable the `pretrained` feature flag. ```toml [dependencies] -yolox-burn = { git = "https://github.com/burn-rs/models", package = "yolox-burn", features = ["pretrained"] } +yolox-burn = { git = "https://github.com/tracel-ai/models", package = "yolox-burn", features = ["pretrained"] } ``` **Important:** this feature requires `std`. From 5e793dfc152fa5892bdeee0dbe57df5f0875c5f0 Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Mon, 15 Apr 2024 10:36:14 -0400 Subject: [PATCH 20/21] Upgrade to Burn 0.13.0 - Removed init_with methods - Fixed empty MaxPool2d vec initialization --- yolox-burn/Cargo.toml | 8 ++--- yolox-burn/src/model/blocks.rs | 57 ------------------------------ yolox-burn/src/model/bottleneck.rs | 37 ++----------------- yolox-burn/src/model/darknet.rs | 26 -------------- yolox-burn/src/model/head.rs | 46 ++---------------------- yolox-burn/src/model/pafpn.rs | 15 -------- yolox-burn/src/model/yolox.rs | 36 ++++++++++--------- 7 files changed, 28 insertions(+), 197 deletions(-) diff --git a/yolox-burn/Cargo.toml b/yolox-burn/Cargo.toml index 5861775..744a2f0 100644 --- a/yolox-burn/Cargo.toml +++ b/yolox-burn/Cargo.toml @@ -12,8 +12,8 @@ pretrained = ["burn/network", "std", "dep:dirs"] [dependencies] # Note: default-features = false is needed to disable std -burn = { git = "https://github.com/tracel-ai/burn.git", default-features = false, rev = "0138e16af6a85c746035632b2a2df0df8098ebc2" } -burn-import = { git = "https://github.com/tracel-ai/burn.git", rev = "0138e16af6a85c746035632b2a2df0df8098ebc2" } +burn = { version = "0.13.0", default-features = false } +burn-import = { version = "0.13.0" } itertools = { version = "0.12.1", default-features = false, features = [ "use_alloc", ] } @@ -24,7 +24,5 @@ serde = { version = "1.0.192", default-features = false, features = [ ] } # alloc is for no_std, derive is needed [dev-dependencies] -burn = { git = "https://github.com/tracel-ai/burn.git", features = [ - "ndarray", -], rev = "0138e16af6a85c746035632b2a2df0df8098ebc2" } +burn = { version = "0.13.0", features = ["ndarray"] } image = { version = "0.24.7", features = ["png", "jpeg"] } diff --git a/yolox-burn/src/model/blocks.rs b/yolox-burn/src/model/blocks.rs index 3aee6f7..320284c 100644 --- a/yolox-burn/src/model/blocks.rs +++ b/yolox-burn/src/model/blocks.rs @@ -69,32 +69,6 @@ impl ConvConfig { ) } } - - /// Initialize a new [convolution block](Conv) module with a [record](ConvRecord). - pub fn init_with(&self, record: ConvRecord) -> Conv { - match record { - ConvRecord::DwsConv(rec) if self.depthwise => Conv::DwsConv( - DwsConvConfig::new( - self.in_channels, - self.out_channels, - self.kernel_size, - self.stride, - ) - .init_with(rec), - ), - ConvRecord::BaseConv(rec) if !self.depthwise => Conv::BaseConv( - BaseConvConfig::new( - self.in_channels, - self.out_channels, - self.kernel_size, - self.stride, - 1, - ) - .init_with(rec), - ), - _ => panic!("Invalid record for depthwise={}", self.depthwise), - } - } } /// A Conv2d -> BatchNorm -> activation block. @@ -150,14 +124,6 @@ impl BaseConvConfig { bn: self.bn.init(device), } } - - /// Initialize a new [base convolution block](BaseConv) module with a [record](BaseConvRecord). - pub fn init_with(&self, record: BaseConvRecord) -> BaseConv { - BaseConv { - conv: self.conv.init_with(record.conv), - bn: self.bn.init_with(record.bn), - } - } } /// A [depthwise separable convolution](https://paperswithcode.com/method/depthwise-separable-convolution) @@ -199,14 +165,6 @@ impl DwsConvConfig { pconv: self.pconv.init(device), } } - - /// Initialize a new [depthwise separable convolution block](DwsConv) module with a [record](DwsConvRecord). - pub fn init_with(&self, record: DwsConvRecord) -> DwsConv { - DwsConv { - dconv: self.dconv.init_with(record.dconv), - pconv: self.pconv.init_with(record.pconv), - } - } } /// Focus width and height information into channel space. @@ -272,13 +230,6 @@ impl FocusConfig { conv: self.conv.init(device), } } - - /// Initialize a new [focus block](Focus) module with a [record](FocusRecord). - pub fn init_with(&self, record: FocusRecord) -> Focus { - Focus { - conv: self.conv.init_with(record.conv), - } - } } /// Dual convolution block used for feature extraction in the prediction head. @@ -317,12 +268,4 @@ impl ConvBlockConfig { conv1: self.conv1.init(device), } } - - /// Initialize a new [dual convolution block](ConvBlock) module with a [record](ConvBlockRecord). - pub fn init_with(&self, record: ConvBlockRecord) -> ConvBlock { - ConvBlock { - conv0: self.conv0.init_with(record.conv0), - conv1: self.conv1.init_with(record.conv1), - } - } } diff --git a/yolox-burn/src/model/bottleneck.rs b/yolox-burn/src/model/bottleneck.rs index 4a7df3e..1b738cd 100644 --- a/yolox-burn/src/model/bottleneck.rs +++ b/yolox-burn/src/model/bottleneck.rs @@ -7,6 +7,8 @@ use burn::{ use super::blocks::{expand, BaseConv, BaseConvConfig, Conv, ConvConfig}; +pub(crate) const SPP_POOLING: [usize; 3] = [5, 9, 13]; + /// Standard bottleneck block. #[derive(Module, Debug)] pub struct Bottleneck { @@ -61,15 +63,6 @@ impl BottleneckConfig { shortcut: self.shortcut, } } - - /// Initialize a new [bottleneck block](Bottleneck) module with a [record](BottleneckRecord). - pub fn init_with(&self, record: BottleneckRecord) -> Bottleneck { - Bottleneck { - conv1: self.conv1.init_with(record.conv1), - conv2: self.conv2.init_with(record.conv2), - shortcut: self.shortcut, - } - } } /// Spatial pyramid pooling layer used in YOLOv3-SPP. @@ -113,7 +106,7 @@ impl SppBottleneckConfig { let conv1 = BaseConvConfig::new(in_channels, hidden_channels, 1, 1, 1); let conv2 = BaseConvConfig::new(conv2_channels, out_channels, 1, 1, 1); - let m: Vec<_> = [5, 9, 13] + let m: Vec<_> = SPP_POOLING .into_iter() .map(|k| { let pad = k / 2; @@ -133,15 +126,6 @@ impl SppBottleneckConfig { m: self.m.iter().map(|m| m.init()).collect(), } } - - /// Initialize a new [bottleneck block](SppBottleneck) module with a [record](SppBottleneckRecord). - pub fn init_with(&self, record: SppBottleneckRecord) -> SppBottleneck { - SppBottleneck { - conv1: self.conv1.init_with(record.conv1), - conv2: self.conv2.init_with(record.conv2), - m: self.m.iter().map(|m| m.init()).collect(), - } - } } /// Simplified Cross Stage Partial bottleneck with 3 convolutional layers. @@ -219,19 +203,4 @@ impl CspBottleneckConfig { m: self.m.iter().map(|b| b.init(device)).collect(), } } - - /// Initialize a new [bottleneck block](CspBottleneck) module with a [record](CspBottleneckRecord). - pub fn init_with(&self, record: CspBottleneckRecord) -> CspBottleneck { - CspBottleneck { - conv1: self.conv1.init_with(record.conv1), - conv2: self.conv2.init_with(record.conv2), - conv3: self.conv3.init_with(record.conv3), - m: self - .m - .iter() - .zip(record.m) - .map(|(b, r)| b.init_with(r)) - .collect(), - } - } } diff --git a/yolox-burn/src/model/darknet.rs b/yolox-burn/src/model/darknet.rs index b17b4b6..5cf8e29 100644 --- a/yolox-burn/src/model/darknet.rs +++ b/yolox-burn/src/model/darknet.rs @@ -110,17 +110,6 @@ impl CspDarknetConfig { dark5: self.dark5.init(device), } } - - /// Initialize a new [CspDarknet](CspDarknet) module with a [record](CspDarknetRecord). - pub fn init_with(&self, record: CspDarknetRecord) -> CspDarknet { - CspDarknet { - stem: self.stem.init_with(record.stem), - dark2: self.dark2.init_with(record.dark2), - dark3: self.dark3.init_with(record.dark3), - dark4: self.dark4.init_with(record.dark4), - dark5: self.dark5.init_with(record.dark5), - } - } } /// A BaseConv -> CspBottleneck block. @@ -180,19 +169,4 @@ impl CspBlockConfig { spp: self.spp.as_ref().map(|spp| spp.init(device)), } } - - /// Initialize a new [CSP block](CspBlock) module with a [record](CspBlockRecord). - pub fn init_with(&self, record: CspBlockRecord) -> CspBlock { - CspBlock { - conv: self.conv.init_with(record.conv), - c3: self.c3.init_with(record.c3), - spp: self.spp.as_ref().map(|d| { - d.init_with( - record - .spp - .expect("Should initialize SppBottleneck block with record."), - ) - }), - } - } } diff --git a/yolox-burn/src/model/head.rs b/yolox-burn/src/model/head.rs index ef60bc6..63e691c 100644 --- a/yolox-burn/src/model/head.rs +++ b/yolox-burn/src/model/head.rs @@ -185,50 +185,8 @@ impl HeadConfig { cls_convs: self.cls_convs.iter().map(|m| m.init(device)).collect(), reg_convs: self.reg_convs.iter().map(|m| m.init(device)).collect(), cls_preds: self.cls_preds.iter().map(|m| m.init(device)).collect(), - reg_preds: self.cls_preds.iter().map(|m| m.init(device)).collect(), - obj_preds: self.cls_preds.iter().map(|m| m.init(device)).collect(), - } - } - - /// Initialize a new [YOLOX head](Head) module with a [record](HeadRecord). - pub fn init_with(&self, record: HeadRecord) -> Head { - Head { - stems: self - .stems - .iter() - .zip(record.stems) - .map(|(m, r)| m.init_with(r)) - .collect(), - cls_convs: self - .cls_convs - .iter() - .zip(record.cls_convs) - .map(|(m, r)| m.init_with(r)) - .collect(), - reg_convs: self - .reg_convs - .iter() - .zip(record.reg_convs) - .map(|(m, r)| m.init_with(r)) - .collect(), - cls_preds: self - .cls_preds - .iter() - .zip(record.cls_preds) - .map(|(m, r)| m.init_with(r)) - .collect(), - reg_preds: self - .reg_preds - .iter() - .zip(record.reg_preds) - .map(|(m, r)| m.init_with(r)) - .collect(), - obj_preds: self - .obj_preds - .iter() - .zip(record.obj_preds) - .map(|(m, r)| m.init_with(r)) - .collect(), + reg_preds: self.reg_preds.iter().map(|m| m.init(device)).collect(), + obj_preds: self.obj_preds.iter().map(|m| m.init(device)).collect(), } } } diff --git a/yolox-burn/src/model/pafpn.rs b/yolox-burn/src/model/pafpn.rs index 4d27a91..c238d0c 100644 --- a/yolox-burn/src/model/pafpn.rs +++ b/yolox-burn/src/model/pafpn.rs @@ -174,19 +174,4 @@ impl PafpnConfig { bu_conv2: self.bu_conv2.init(device), } } - - /// Initialize a new [PAFPN](Pafpn) module with a [record](PafpnRecord). - pub fn init_with(&self, record: PafpnRecord) -> Pafpn { - Pafpn { - backbone: self.backbone.init_with(record.backbone), - lateral_conv0: self.lateral_conv0.init_with(record.lateral_conv0), - c3_n3: self.c3_n3.init_with(record.c3_n3), - c3_n4: self.c3_n4.init_with(record.c3_n4), - c3_p3: self.c3_p3.init_with(record.c3_p3), - c3_p4: self.c3_p4.init_with(record.c3_p4), - reduce_conv1: self.reduce_conv1.init_with(record.reduce_conv1), - bu_conv1: self.bu_conv1.init_with(record.bu_conv1), - bu_conv2: self.bu_conv2.init_with(record.bu_conv2), - } - } } diff --git a/yolox-burn/src/model/yolox.rs b/yolox-burn/src/model/yolox.rs index 73cac2e..8c551a7 100644 --- a/yolox-burn/src/model/yolox.rs +++ b/yolox-burn/src/model/yolox.rs @@ -1,8 +1,10 @@ use burn::{ - module::Module, + module::{ConstantRecord, Module}, tensor::{backend::Backend, Device, Tensor}, }; +use crate::model::bottleneck::SPP_POOLING; + use super::{ head::{Head, HeadConfig}, pafpn::{Pafpn, PafpnConfig}, @@ -61,7 +63,7 @@ impl Yolox { let weights = weights.weights(); let record = Self::load_weights_record(&weights, device)?; - let model = YoloxConfig::new(0.33, 0.25, weights.num_classes, true).init_with(record); + let model = Self::yolox_nano(weights.num_classes, device).load_record(record); Ok(model) } @@ -99,7 +101,7 @@ impl Yolox { let weights = weights.weights(); let record = Self::load_weights_record(&weights, device)?; - let model = YoloxConfig::new(0.33, 0.375, weights.num_classes, false).init_with(record); + let model = Self::yolox_tiny(weights.num_classes, device).load_record(record); Ok(model) } @@ -137,7 +139,7 @@ impl Yolox { let weights = weights.weights(); let record = Self::load_weights_record(&weights, device)?; - let model = YoloxConfig::new(0.33, 0.50, weights.num_classes, false).init_with(record); + let model = Self::yolox_s(weights.num_classes, device).load_record(record); Ok(model) } @@ -175,7 +177,7 @@ impl Yolox { let weights = weights.weights(); let record = Self::load_weights_record(&weights, device)?; - let model = YoloxConfig::new(0.67, 0.75, weights.num_classes, false).init_with(record); + let model = Self::yolox_m(weights.num_classes, device).load_record(record); Ok(model) } @@ -213,7 +215,7 @@ impl Yolox { let weights = weights.weights(); let record = Self::load_weights_record(&weights, device)?; - let model = YoloxConfig::new(1., 1., weights.num_classes, false).init_with(record); + let model = Self::yolox_l(weights.num_classes, device).load_record(record); Ok(model) } @@ -251,7 +253,7 @@ impl Yolox { let weights = weights.weights(); let record = Self::load_weights_record(&weights, device)?; - let model = YoloxConfig::new(1.33, 1.25, weights.num_classes, false).init_with(record); + let model = Self::yolox_x(weights.num_classes, device).load_record(record); Ok(model) } @@ -286,7 +288,17 @@ impl Yolox { "$1.conv$3.$4", ); - let record = PyTorchFileRecorder::::new().load(load_args, device)?; + let mut record: YoloxRecord = + PyTorchFileRecorder::::new().load(load_args, device)?; + + if let Some(ref mut spp) = record.backbone.backbone.dark5.spp { + // Handle the initialization for Vec, which has no parameters. + // Without this, the vector would be initialized as empty and thus no MaxPool2d + // layers would be applied, which is incorrect. + if spp.m.is_empty() { + spp.m = vec![ConstantRecord; SPP_POOLING.len()]; + } + } Ok(record) } @@ -314,12 +326,4 @@ impl YoloxConfig { head: self.head.init(device), } } - - /// Initialize a new [YOLOX detector](Yolox) module with a [record](YoloxRecord). - pub fn init_with(&self, record: YoloxRecord) -> Yolox { - Yolox { - backbone: self.backbone.init_with(record.backbone), - head: self.head.init_with(record.head), - } - } } From 752aa8cf398231e7830b694709fd1c88f5e60423 Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Thu, 25 Apr 2024 11:29:13 -0400 Subject: [PATCH 21/21] Update image version --- yolox-burn/Cargo.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/yolox-burn/Cargo.toml b/yolox-burn/Cargo.toml index 744a2f0..e27bd68 100644 --- a/yolox-burn/Cargo.toml +++ b/yolox-burn/Cargo.toml @@ -25,4 +25,4 @@ serde = { version = "1.0.192", default-features = false, features = [ [dev-dependencies] burn = { version = "0.13.0", features = ["ndarray"] } -image = { version = "0.24.7", features = ["png", "jpeg"] } +image = { version = "0.24.9", features = ["png", "jpeg"] }

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zs*UBmhU`b5yfC}1r7OYR&0E0D)+2T}51Yb^x5f#gwg7vZV0SG(esC3#f|{KnBy+qR zA6v0s_iS0jZOn9V;5x;t<3I&dW~_P2a3&=Mj?7kg0Jqx-Aw&87?1tlnkOv;ugC}=e z$q1^U?^-JHK4nU%{U%z6dNq0*8OA%C>e9hDPWp>h=hSO}WbP4-umx?Uo zxXLjN^pG6{gtQuGbcBdaIhPfT!C%j-mU#p;E*2|Y76Y)`z1Gr2yeU3QO<2Z z;me7-LgjSXDSn}SLRk8u^VR1~cN-xQPpo8I?sk)k&$Mf|XKzW*p%u~}Eir{L@N}s{XNh!uV^5(8E2aDTrG?Chjm<5Hf;;S?Je8`<5y04M9r%vxB9&yk&AM`oX-N+ zYpi;ycG9D-jx+3;@TRv31O+@IcS#N7PigXC|AwDqJ;@sJqS!s~e&c0!S^<6#-7KVJl`T4eyd^ usize { + (num_channels as f64 * factor).floor() as usize +} + +/// A Conv2d -> BatchNorm -> activation block. +#[derive(Module, Debug)] +pub struct BaseConv { + conv: Conv2d, + bn: BatchNorm, +} + +impl BaseConv { + pub fn forward(&self, x: Tensor) -> Tensor { + let x = self.conv.forward(x); + let x = self.bn.forward(x); + + // SiLU not in nn yet + silu(x) + } +} + +/// [Base convolution block](BaseConv) configuration. +pub struct BaseConvConfig { + conv: Conv2dConfig, + bn: BatchNormConfig, +} + +impl BaseConvConfig { + /// Create a new instance of the base convolution block [config](BaseConvConfig). + pub fn new( + in_channels: usize, + out_channels: usize, + kernel_size: usize, + stride: usize, + groups: usize, + ) -> Self { + // Same padding + let pad = (kernel_size - 1) / 2; + + let conv = Conv2dConfig::new([in_channels, out_channels], [kernel_size, kernel_size]) + .with_stride([stride, stride]) + .with_padding(PaddingConfig2d::Explicit(pad, pad)) + .with_groups(groups) + .with_bias(false); + let bn = BatchNormConfig::new(out_channels) + .with_epsilon(1e-3) + .with_momentum(0.03); + + Self { conv, bn } + } + + /// Initialize a new [base convolution block](BaseConv) module. + pub fn init(&self, device: &Device) -> BaseConv { + BaseConv { + conv: self.conv.init(device), + bn: self.bn.init(device), + } + } + + /// Initialize a new [base convolution block](BaseConv) module with a [record](BaseConvRecord). + pub fn init_with(&self, record: BaseConvRecord) -> BaseConv { + BaseConv { + conv: self.conv.init_with(record.conv), + bn: self.bn.init_with(record.bn), + } + } +} + +/// A [depthwise separable convolution](https://paperswithcode.com/method/depthwise-separable-convolution) block. +#[derive(Module, Debug)] +pub struct DwsConv { + dconv: BaseConv, + pconv: BaseConv, +} + +impl DwsConv { + pub fn forward(&self, x: Tensor) -> Tensor { + let x = self.dconv.forward(x); + self.pconv.forward(x) + } +} + +/// [Depthwise separable convolution block](DwsConv) configuration. +pub struct DwsConvConfig { + dconv: BaseConvConfig, + pconv: BaseConvConfig, +} + +impl DwsConvConfig { + /// Create a new instance of the depthwise separable convolution block [config](DwsConvConfig). + pub fn new(in_channels: usize, out_channels: usize, kernel_size: usize, stride: usize) -> Self { + // Depthwise conv + let dconv = BaseConvConfig::new(in_channels, in_channels, kernel_size, stride, in_channels); + // Pointwise conv + let pconv = BaseConvConfig::new(in_channels, out_channels, 1, 1, 1); + + Self { dconv, pconv } + } + + /// Initialize a new [depthwise separable convolution block](DwsConv) module. + pub fn init(&self, device: &Device) -> DwsConv { + DwsConv { + dconv: self.dconv.init(device), + pconv: self.pconv.init(device), + } + } + + /// Initialize a new [depthwise separable convolution block](DwsConv) module with a [record](DwsConvRecord). + pub fn init_with(&self, record: DwsConvRecord) -> DwsConv { + DwsConv { + dconv: self.dconv.init_with(record.dconv), + pconv: self.pconv.init_with(record.pconv), + } + } +} + +/// Focus width and height information into channel space. +#[derive(Module, Debug)] +pub struct Focus { + conv: BaseConv, +} + +impl Focus { + pub fn forward(&self, x: Tensor) -> Tensor { + let device = x.device(); + let [_, _, h, w] = x.shape().dims; + + // Indexing + let top_idx = Tensor::arange_step(0..h as i64, 2, &device); + let bottom_idx = Tensor::arange_step(1..h as i64, 2, &device); + let left_idx = Tensor::arange_step(0..w as i64, 2, &device); + let right_idx = Tensor::arange_step(1..w as i64, 2, &device); + + // patch_top_left = x[..., ::2, ::2] + let patch_top_left = x + .clone() + .select(2, top_idx.clone()) + .select(3, left_idx.clone()); + // patch_top_right = x[..., ::2, 1::2] + let patch_top_right = x.clone().select(2, top_idx).select(3, right_idx.clone()); + // patch_bot_left = x[..., 1::2, ::2] + let patch_bottom_left = x.clone().select(2, bottom_idx.clone()).select(3, left_idx); + // patch_bot_right = x[..., 1::2, 1::2] + let patch_bottom_right = x.select(2, bottom_idx).select(3, right_idx); + + // Shape (b,c,w,h) -> y(b,4c,w/2,h/2) + let x = Tensor::cat( + vec![ + patch_top_left, + patch_bottom_left, + patch_top_right, + patch_bottom_right, + ], + 1, + ); + + let x = self.conv.forward(x); + x + } +} + +/// [Focus block](Focus) configuration. +pub struct FocusConfig { + conv: BaseConvConfig, +} + +impl FocusConfig { + /// Create a new instance of the focus block [config](FocusConfig). + pub fn new(in_channels: usize, out_channels: usize, kernel_size: usize, stride: usize) -> Self { + let conv = BaseConvConfig::new(in_channels * 4, out_channels, kernel_size, stride, 1); + + Self { conv } + } + + /// Initialize a new [focus block](Focus) module. + pub fn init(&self, device: &Device) -> Focus { + Focus { + conv: self.conv.init(device), + } + } + + /// Initialize a new [focus block](Focus) module with a [record](FocusRecord). + pub fn init_with(&self, record: FocusRecord) -> Focus { + Focus { + conv: self.conv.init_with(record.conv), + } + } +} + +/// Dual convolution block used for feature extraction in the prediction head. +#[derive(Module, Debug)] +pub struct ConvBlock { + conv0: BaseConv, + conv1: BaseConv, +} + +impl ConvBlock { + pub fn forward(&self, x: Tensor) -> Tensor { + let x = self.conv0.forward(x); + self.conv1.forward(x) + } +} + +/// [Dual convolution block](ConvBlock) configuration. +pub struct ConvBlockConfig { + conv0: BaseConvConfig, + conv1: BaseConvConfig, +} + +impl ConvBlockConfig { + /// Create a new instance of the dual convolution block [config](ConvBlockConfig). + pub fn new(channels: usize, kernel_size: usize, stride: usize) -> Self { + let conv0 = BaseConvConfig::new(channels, channels, kernel_size, stride, 1); + let conv1 = BaseConvConfig::new(channels, channels, kernel_size, stride, 1); + + Self { conv0, conv1 } + } + + /// Initialize a new [dual convolution block](ConvBlock) module. + pub fn init(&self, device: &Device) -> ConvBlock { + ConvBlock { + conv0: self.conv0.init(device), + conv1: self.conv1.init(device), + } + } + + /// Initialize a new [dual convolution block](ConvBlock) module with a [record](ConvBlockRecord). + pub fn init_with(&self, record: ConvBlockRecord) -> ConvBlock { + ConvBlock { + conv0: self.conv0.init_with(record.conv0), + conv1: self.conv1.init_with(record.conv1), + } + } +} diff --git a/yolox-burn/src/model/bottleneck.rs b/yolox-burn/src/model/bottleneck.rs new file mode 100644 index 0000000..403a165 --- /dev/null +++ b/yolox-burn/src/model/bottleneck.rs @@ -0,0 +1,240 @@ +use alloc::{vec, vec::Vec}; +use burn::{ + module::Module, + nn::pool::{MaxPool2d, MaxPool2dConfig}, + tensor::{backend::Backend, Device, Tensor}, +}; + +use super::blocks::{expand, BaseConv, BaseConvConfig}; + +/// Standard bottleneck block. +#[derive(Module, Debug)] +pub struct Bottleneck { + conv1: BaseConv, + conv2: BaseConv, + shortcut: bool, +} + +impl Bottleneck { + pub fn forward(&self, x: Tensor) -> Tensor { + let identity = x.clone(); + + let x = self.conv1.forward(x); + let mut x = self.conv2.forward(x); + + if self.shortcut { + x = x + identity; + } + + x + } +} + +/// [Bottleneck block](Bottleneck) configuration. +struct BottleneckConfig { + conv1: BaseConvConfig, + conv2: BaseConvConfig, + shortcut: bool, +} + +impl BottleneckConfig { + /// Create a new instance of the bottleneck block [config](BottleneckConfig). + pub fn new(in_channels: usize, out_channels: usize, shortcut: bool) -> Self { + // In practice, expansion = 1.0 and no shortcut connection is used + let hidden_channels = out_channels; + + let conv1 = BaseConvConfig::new(in_channels, hidden_channels, 1, 1, 1); + let conv2 = BaseConvConfig::new(hidden_channels, out_channels, 3, 1, 1); + + Self { + conv1, + conv2, + shortcut, + } + } + + /// Initialize a new [bottleneck block](Bottleneck) module. + pub fn init(&self, device: &Device) -> Bottleneck { + Bottleneck { + conv1: self.conv1.init(device), + conv2: self.conv2.init(device), + shortcut: self.shortcut, + } + } + + /// Initialize a new [bottleneck block](Bottleneck) module with a [record](BottleneckRecord). + pub fn init_with(&self, record: BottleneckRecord) -> Bottleneck { + Bottleneck { + conv1: self.conv1.init_with(record.conv1), + conv2: self.conv2.init_with(record.conv2), + shortcut: self.shortcut, + } + } +} + +/// Spatial pyramid pooling layer used in YOLOv3-SPP. +#[derive(Module, Debug)] +pub struct SppBottleneck { + conv1: BaseConv, + conv2: BaseConv, + m: Vec, +} + +impl SppBottleneck { + pub fn forward(&self, x: Tensor) -> Tensor { + if self.m.len() == 0 { + panic!("No MaxPool2d modules found"); + } + + let x = self.conv1.forward(x); + + let x: Vec<_> = vec![x.clone()] + .into_iter() + .chain(self.m.iter().map(|pool| pool.forward(x.clone()))) + .collect(); + let x = Tensor::cat(x, 1); + + self.conv2.forward(x) + } +} + +/// [SppBottleneck block](SppBottleneck) configuration. +pub struct SppBottleneckConfig { + conv1: BaseConvConfig, + conv2: BaseConvConfig, + m: Vec, +} + +impl SppBottleneckConfig { + /// Create a new instance of the bottleneck block [config](SppBottleneckConfig). + pub fn new(in_channels: usize, out_channels: usize) -> Self { + let hidden_channels = in_channels / 2; + let conv2_channels = hidden_channels * 4; // conv1 output + maxpool (3x) + + let conv1 = BaseConvConfig::new(in_channels, hidden_channels, 1, 1, 1); + let conv2 = BaseConvConfig::new(conv2_channels, out_channels, 1, 1, 1); + let m: Vec<_> = [5, 9, 13] + .into_iter() + .map(|k| { + let pad = k / 2; + MaxPool2dConfig::new([k, k]) + .with_padding(burn::nn::PaddingConfig2d::Explicit(pad, pad)) + }) + .collect(); + + Self { conv1, conv2, m } + } + + /// Initialize a new [bottleneck block](SppBottleneck) module. + pub fn init(&self, device: &Device) -> SppBottleneck { + SppBottleneck { + conv1: self.conv1.init(device), + conv2: self.conv2.init(device), + m: self.m.iter().map(|m| m.init()).collect(), + } + } + + /// Initialize a new [bottleneck block](SppBottleneck) module with a [record](SppBottleneckRecord). + pub fn init_with(&self, record: SppBottleneckRecord) -> SppBottleneck { + SppBottleneck { + conv1: self.conv1.init_with(record.conv1), + conv2: self.conv2.init_with(record.conv2), + m: self.m.iter().map(|m| m.init()).collect(), + } + } +} + +/// Simplified Cross Stage Partial bottleneck with 3 convolutional layers. +/// Equivalent to C3 in YOLOv5. +#[derive(Module, Debug)] +pub struct CspBottleneck { + conv1: BaseConv, + conv2: BaseConv, + conv3: BaseConv, + m: Vec>, +} + +impl CspBottleneck { + pub fn forward(&self, x: Tensor) -> Tensor { + let x1 = self.conv1.forward(x.clone()); + let x2 = self.conv2.forward(x); + + let x1 = self + .m + .iter() + .fold(x1, |x_i, bottleneck| bottleneck.forward(x_i)); + // let mut x1 = x1; + // for bottleneck in &self.m { + // x1 = bottleneck.forward(x1); + // } + + let x = Tensor::cat(vec![x1, x2], 1); + + self.conv3.forward(x) + } +} + +/// [CspBottleneck block](CspBottleneck) configuration. +pub struct CspBottleneckConfig { + conv1: BaseConvConfig, + conv2: BaseConvConfig, + conv3: BaseConvConfig, + m: Vec, +} + +impl CspBottleneckConfig { + /// Create a new instance of the bottleneck block [config](CspBottleneckConfig). + pub fn new( + in_channels: usize, + out_channels: usize, + num_blocks: usize, + expansion: f64, + shortcut: bool, + ) -> Self { + assert!( + expansion > 0.0 && expansion <= 1.0, + "expansion should be in range (0, 1]" + ); + + let hidden_channels = expand(out_channels, expansion); + + let conv1 = BaseConvConfig::new(in_channels, hidden_channels, 1, 1, 1); + let conv2 = BaseConvConfig::new(in_channels, hidden_channels, 1, 1, 1); + let conv3 = BaseConvConfig::new(2 * hidden_channels, out_channels, 1, 1, 1); + let m = (0..num_blocks) + .map(|_| BottleneckConfig::new(hidden_channels, hidden_channels, shortcut)) + .collect(); + + Self { + conv1, + conv2, + conv3, + m, + } + } + + /// Initialize a new [bottleneck block](CspBottleneck) module. + pub fn init(&self, device: &Device) -> CspBottleneck { + CspBottleneck { + conv1: self.conv1.init(device), + conv2: self.conv2.init(device), + conv3: self.conv3.init(device), + m: self.m.iter().map(|b| b.init(device)).collect(), + } + } + + /// Initialize a new [bottleneck block](CspBottleneck) module with a [record](CspBottleneckRecord). + pub fn init_with(&self, record: CspBottleneckRecord) -> CspBottleneck { + CspBottleneck { + conv1: self.conv1.init_with(record.conv1), + conv2: self.conv2.init_with(record.conv2), + conv3: self.conv3.init_with(record.conv3), + m: self + .m + .iter() + .zip(record.m) + .map(|(b, r)| b.init_with(r)) + .collect(), + } + } +} diff --git a/yolox-burn/src/model/darknet.rs b/yolox-burn/src/model/darknet.rs new file mode 100644 index 0000000..cc6b84a --- /dev/null +++ b/yolox-burn/src/model/darknet.rs @@ -0,0 +1,170 @@ +use core::cmp::max; + +use crate::model::blocks::expand; + +use super::{ + blocks::{BaseConv, BaseConvConfig, Focus, FocusConfig}, + bottleneck::{CspBottleneck, CspBottleneckConfig, SppBottleneck, SppBottleneckConfig}, +}; +use burn::{ + module::Module, + tensor::{backend::Backend, Device, Tensor}, +}; + +/// Darknet backbone feature maps. +pub struct DarknetFeatures(pub Tensor, pub Tensor, pub Tensor); + +/// [CSPDarknet-53](https://paperswithcode.com/method/cspdarknet53) backbone. +#[derive(Module, Debug)] +pub struct CspDarknet { + stem: Focus, + dark2: CspBlock, + dark3: CspBlock, + dark4: CspBlock, + dark5: CspBlock, +} + +impl CspDarknet { + pub fn forward(&self, x: Tensor) -> DarknetFeatures { + let x = self.stem.forward(x); + let x = self.dark2.forward(x); + let f1 = self.dark3.forward(x); + let f2 = self.dark4.forward(f1.clone()); + let f3 = self.dark5.forward(f2.clone()); + + DarknetFeatures(f1, f2, f3) + } +} + +/// [CSPDarknet-53](CspDarknet) configuration. +pub struct CspDarknetConfig { + stem: FocusConfig, + dark2: CspBlockConfig, + dark3: CspBlockConfig, + dark4: CspBlockConfig, + dark5: CspBlockConfig, +} + +impl CspDarknetConfig { + /// Create a new instance of the CSPDarknet-53 [config](CspDarknetConfig). + pub fn new(depth: f64, width: f64) -> Self { + assert!( + [0.33, 0.67, 1.0, 1.33].contains(&depth), + "invalid depth value {depth}" + ); + + assert!( + [0.25, 0.375, 0.5, 0.75, 1.0, 1.25].contains(&width), + "invalid width value {width}" + ); + + let base_channels = expand(64, width); + let base_depth = max((depth * 3_f64).round() as usize, 1); + + let stem = FocusConfig::new(3, base_channels, 3, 1); + let dark2 = CspBlockConfig::new(base_channels, base_channels * 2, base_depth, false); + let dark3 = + CspBlockConfig::new(base_channels * 2, base_channels * 4, base_depth * 3, false); + let dark4 = + CspBlockConfig::new(base_channels * 4, base_channels * 8, base_depth * 3, false); + let dark5 = CspBlockConfig::new(base_channels * 8, base_channels * 16, base_depth, true); + + Self { + stem, + dark2, + dark3, + dark4, + dark5, + } + } + + /// Initialize a new [CspDarknet](CspDarknet) module. + pub fn init(&self, device: &Device) -> CspDarknet { + CspDarknet { + stem: self.stem.init(device), + dark2: self.dark2.init(device), + dark3: self.dark3.init(device), + dark4: self.dark4.init(device), + dark5: self.dark5.init(device), + } + } + + /// Initialize a new [CspDarknet](CspDarknet) module with a [record](CspDarknetRecord). + pub fn init_with(&self, record: CspDarknetRecord) -> CspDarknet { + CspDarknet { + stem: self.stem.init_with(record.stem), + dark2: self.dark2.init_with(record.dark2), + dark3: self.dark3.init_with(record.dark3), + dark4: self.dark4.init_with(record.dark4), + dark5: self.dark5.init_with(record.dark5), + } + } +} + +/// A BaseConv -> CspBottleneck block. +/// The SppBottleneck layer is only used in the last block of [CSPDarknet-53](CspDarknet). +#[derive(Module, Debug)] +pub struct CspBlock { + conv: BaseConv, + c3: CspBottleneck, + spp: Option>, +} + +impl CspBlock { + pub fn forward(&self, x: Tensor) -> Tensor { + let mut x = self.conv.forward(x); + + if let Some(spp) = &self.spp { + x = spp.forward(x); + } + + self.c3.forward(x) + } +} + +/// [CSP block](CspBlock) configuration. +pub struct CspBlockConfig { + conv: BaseConvConfig, + c3: CspBottleneckConfig, + spp: Option, +} + +impl CspBlockConfig { + /// Create a new instance of the CSP block [config](CspBlockConfig). + pub fn new(in_channels: usize, out_channels: usize, depth: usize, spp: bool) -> Self { + let conv = BaseConvConfig::new(in_channels, out_channels, 3, 2, 1); + let c3 = CspBottleneckConfig::new(out_channels, out_channels, depth, 0.5, !spp); + + let spp = if spp { + Some(SppBottleneckConfig::new(out_channels, out_channels)) + } else { + None + }; + + Self { conv, c3, spp } + } + + /// Initialize a new [CSP block](CspBlock) module. + pub fn init(&self, device: &Device) -> CspBlock { + CspBlock { + conv: self.conv.init(device), + c3: self.c3.init(device), + spp: self.spp.as_ref().map(|spp| spp.init(device)), + } + } + + /// Initialize a new [CSP block](CspBlock) module with a [record](CspBlockRecord). + pub fn init_with(&self, record: CspBlockRecord) -> CspBlock { + CspBlock { + conv: self.conv.init_with(record.conv), + c3: self.c3.init_with(record.c3), + spp: self.spp.as_ref().map(|d| { + d.init_with( + record + .spp + .expect("Should initialize SppBottleneck block with record."), + ) + }), + } + } +} diff --git a/yolox-burn/src/model/head.rs b/yolox-burn/src/model/head.rs new file mode 100644 index 0000000..73c7606 --- /dev/null +++ b/yolox-burn/src/model/head.rs @@ -0,0 +1,235 @@ +use alloc::{vec, vec::Vec}; +use burn::{ + module::Module, + nn::{ + conv::{Conv2d, Conv2dConfig}, + Initializer, PaddingConfig2d, + }, + tensor::{activation::sigmoid, backend::Backend, Device, Int, Shape, Tensor}, +}; +use itertools::{izip, multiunzip}; + +use super::{ + blocks::{expand, BaseConv, BaseConvConfig, ConvBlock, ConvBlockConfig}, + pafpn::FpnFeatures, +}; + +const STRIDES: [usize; 3] = [8, 16, 32]; +const IN_CHANNELS: [usize; 3] = [256, 512, 1024]; +const PRIOR_PROB: f64 = 1e-2; + +/// Create a 2D coordinate grid for the specified dimensions. +/// Similar to [`numpy.indices`](https://numpy.org/doc/stable/reference/generated/numpy.indices.html) +/// but specific to two dimensions. +fn create_2d_grid(x: usize, y: usize, device: &Device) -> Tensor { + let y_idx = Tensor::arange(0..y as i64, device) + .repeat(0, x) + .reshape(Shape::new([y, x])); + let x_idx = Tensor::arange(0..x as i64, device) + .repeat(0, y) + .reshape(Shape::new([x, y])) + .transpose(); + + Tensor::stack(vec![x_idx, y_idx], 2) +} + +/// YOLOX head. +#[derive(Module, Debug)] +pub struct Head { + stems: Vec>, + cls_convs: Vec>, + reg_convs: Vec>, + cls_preds: Vec>, + reg_preds: Vec>, + obj_preds: Vec>, +} + +impl Head { + pub fn forward(&self, x: FpnFeatures) -> Tensor { + let features: [Tensor; 3] = [x.0, x.1, x.2]; + + // Outputs for each feature map + let (outputs, shapes): (Vec>, Vec<(usize, usize)>) = izip!( + features, + &self.stems, + &self.cls_convs, + &self.cls_preds, + &self.reg_convs, + &self.reg_preds, + &self.obj_preds, + &STRIDES + ) + .map( + |(feat, stem, cls_conv, cls_pred, reg_conv, reg_pred, obj_pred, _stride)| { + let feat = stem.forward(feat); + + let cls_feat = cls_conv.forward(feat.clone()); + let cls_out = cls_pred.forward(cls_feat); + + let reg_feat = reg_conv.forward(feat); + let reg_out = reg_pred.forward(reg_feat.clone()); + + let obj_out = obj_pred.forward(reg_feat); + + // Output [B, 5 + num_classes, num_anchors] + let out = Tensor::cat(vec![reg_out, sigmoid(obj_out), sigmoid(cls_out)], 1); + let [_, _, h, w] = out.shape().dims; + (out.flatten(2, 3), (h, w)) + }, + ) + .unzip(); + + // 1. Concat all regression outputs + // 2. Permute shape to [B, num_anchors_total, 5 + num_classes] + // 3. Decode absolute bounding box values + self.decode(Tensor::cat(outputs, 2).swap_dims(2, 1), shapes.as_ref()) + } + + /// Decode bounding box absolute values from regression output offsets. + fn decode(&self, outputs: Tensor, shapes: &[(usize, usize)]) -> Tensor { + let device = outputs.device(); + let [b, num_anchors, num_outputs] = outputs.shape().dims; + + let (grids, strides) = shapes + .iter() + .zip(STRIDES) + .map(|((h, w), stride)| { + // Grid (x, y) coordinates + let num_anchors = w * h; + let grid = + create_2d_grid::(*w, *h, &device).reshape(Shape::new([1, num_anchors, 2])); + let strides: Tensor = + Tensor::full(Shape::new([1, num_anchors, 1]), stride as i64, &device); + + (grid, strides) + }) + .unzip(); + + let grids = Tensor::cat(grids, 1).float(); + let strides = Tensor::cat(strides, 1).float(); + + Tensor::cat( + vec![ + // Add grid offset to center coordinates and scale to image dimensions + (outputs.clone().slice([0..b, 0..num_anchors, 0..2]) + grids) * strides.clone(), + // Decode `log` encoded boxes with `exp`and scale to image dimensions + outputs.clone().slice([0..b, 0..num_anchors, 2..4]).exp() * strides, + // Classification outputs + outputs.slice([0..b, 0..num_anchors, 4..num_outputs]), + ], + 2, + ) + } + + // TODO + // Training outputs + // fn forward_train(&self, x: FpnFeatures) {} +} + +/// [YOLOX head](Head) configuration. +pub struct HeadConfig { + stems: Vec, + cls_convs: Vec, + reg_convs: Vec, + cls_preds: Vec, + reg_preds: Vec, + obj_preds: Vec, +} + +impl HeadConfig { + /// Create a new instance of the YOLOX head [config](HeadConfig). + pub fn new(num_classes: usize, width: f64) -> Self { + let hidden_channels: usize = 256; + // Initialize conv2d biases for classification and objectness heads + let bias = -f64::ln((1.0 - PRIOR_PROB) / PRIOR_PROB); + + let (stems, cls_convs, reg_convs, cls_preds, reg_preds, obj_preds) = + multiunzip(IN_CHANNELS.into_iter().map(|in_channels| { + let stem = BaseConvConfig::new( + expand(in_channels, width), + expand(hidden_channels, width), + 1, + 1, + 1, + ); + + let cls_conv = ConvBlockConfig::new(expand(hidden_channels, width), 3, 1); + let reg_conv = ConvBlockConfig::new(expand(hidden_channels, width), 3, 1); + + let cls_pred = + Conv2dConfig::new([expand(hidden_channels, width), num_classes], [1, 1]) + .with_padding(PaddingConfig2d::Explicit(0, 0)) + .with_initializer(Initializer::Constant { value: bias }); + let reg_pred = Conv2dConfig::new([expand(hidden_channels, width), 4], [1, 1]) + .with_padding(PaddingConfig2d::Explicit(0, 0)); + let obj_pred = Conv2dConfig::new([expand(hidden_channels, width), 1], [1, 1]) + .with_padding(PaddingConfig2d::Explicit(0, 0)) + .with_initializer(Initializer::Constant { value: bias }); + + (stem, cls_conv, reg_conv, cls_pred, reg_pred, obj_pred) + })); + + Self { + stems, + cls_convs, + reg_convs, + cls_preds, + reg_preds, + obj_preds, + } + } + + /// Initialize a new [YOLOX head](Head) module. + pub fn init(&self, device: &Device) -> Head { + Head { + stems: self.stems.iter().map(|m| m.init(device)).collect(), + cls_convs: self.cls_convs.iter().map(|m| m.init(device)).collect(), + reg_convs: self.reg_convs.iter().map(|m| m.init(device)).collect(), + cls_preds: self.cls_preds.iter().map(|m| m.init(device)).collect(), + reg_preds: self.cls_preds.iter().map(|m| m.init(device)).collect(), + obj_preds: self.cls_preds.iter().map(|m| m.init(device)).collect(), + } + } + + /// Initialize a new [YOLOX head](Head) module with a [record](HeadRecord). + pub fn init_with(&self, record: HeadRecord) -> Head { + Head { + stems: self + .stems + .iter() + .zip(record.stems) + .map(|(m, r)| m.init_with(r)) + .collect(), + cls_convs: self + .cls_convs + .iter() + .zip(record.cls_convs) + .map(|(m, r)| m.init_with(r)) + .collect(), + reg_convs: self + .reg_convs + .iter() + .zip(record.reg_convs) + .map(|(m, r)| m.init_with(r)) + .collect(), + cls_preds: self + .cls_preds + .iter() + .zip(record.cls_preds) + .map(|(m, r)| m.init_with(r)) + .collect(), + reg_preds: self + .reg_preds + .iter() + .zip(record.reg_preds) + .map(|(m, r)| m.init_with(r)) + .collect(), + obj_preds: self + .obj_preds + .iter() + .zip(record.obj_preds) + .map(|(m, r)| m.init_with(r)) + .collect(), + } + } +} diff --git a/yolox-burn/src/model/mod.rs b/yolox-burn/src/model/mod.rs new file mode 100644 index 0000000..1649d47 --- /dev/null +++ b/yolox-burn/src/model/mod.rs @@ -0,0 +1,7 @@ +mod blocks; +mod bottleneck; +mod darknet; +mod head; +mod pafpn; +pub mod weights; +pub mod yolox; diff --git a/yolox-burn/src/model/pafpn.rs b/yolox-burn/src/model/pafpn.rs new file mode 100644 index 0000000..573a652 --- /dev/null +++ b/yolox-burn/src/model/pafpn.rs @@ -0,0 +1,168 @@ +use alloc::vec; +use burn::{ + module::Module, + tensor::{ + backend::Backend, + module::interpolate, + ops::{InterpolateMode, InterpolateOptions}, + Device, Tensor, + }, +}; + +use super::{ + blocks::{expand, BaseConv, BaseConvConfig}, + bottleneck::{CspBottleneck, CspBottleneckConfig}, + darknet::{CspDarknet, CspDarknetConfig}, +}; + +pub struct FpnFeatures(pub Tensor, pub Tensor, pub Tensor); + +/// [PAFPN](https://paperswithcode.com/method/pafpn) is the feature pyramid module used in +/// [Path Aggregation Network](https://arxiv.org/abs/1803.01534) that combines FPNs with +/// bottom-up path augmentation. +#[derive(Module, Debug)] +pub struct Pafpn { + backbone: CspDarknet, + lateral_conv0: BaseConv, + c3_n3: CspBottleneck, + c3_n4: CspBottleneck, + c3_p3: CspBottleneck, + c3_p4: CspBottleneck, + reduce_conv1: BaseConv, + bu_conv1: BaseConv, // bottom-up conv + bu_conv2: BaseConv, // bottom-up conv +} + +impl Pafpn { + pub fn forward(&self, x: Tensor) -> FpnFeatures { + fn upsample(x_in: Tensor, scale: usize) -> Tensor { + let [_, _, h, w] = x_in.shape().dims; + interpolate( + x_in, + [h * scale, w * scale], + InterpolateOptions::new(InterpolateMode::Nearest), + ) + } + + // Backbone features + let features = self.backbone.forward(x); + + let fpn_out0 = self.lateral_conv0.forward(features.2); + let f_out0 = upsample(fpn_out0.clone(), 2); + let f_out0 = Tensor::cat(vec![f_out0, features.1], 1); + let f_out0 = self.c3_p4.forward(f_out0); + + let fpn_out1 = self.reduce_conv1.forward(f_out0); + let f_out1 = upsample(fpn_out1.clone(), 2); + let f_out1 = Tensor::cat(vec![f_out1, features.0], 1); + let pan_out2 = self.c3_p3.forward(f_out1); + + let p_out1 = self.bu_conv2.forward(pan_out2.clone()); + let p_out1 = Tensor::cat(vec![p_out1, fpn_out1], 1); + let pan_out1 = self.c3_n3.forward(p_out1); + + let p_out0 = self.bu_conv1.forward(pan_out1.clone()); + let p_out0 = Tensor::cat(vec![p_out0, fpn_out0], 1); + let pan_out0 = self.c3_n4.forward(p_out0); + + FpnFeatures(pan_out2, pan_out1, pan_out0) + } +} + +/// [PAFPN block](Pafpn) configuration. +pub struct PafpnConfig { + backbone: CspDarknetConfig, + lateral_conv0: BaseConvConfig, + c3_n3: CspBottleneckConfig, + c3_n4: CspBottleneckConfig, + c3_p3: CspBottleneckConfig, + c3_p4: CspBottleneckConfig, + reduce_conv1: BaseConvConfig, + bu_conv1: BaseConvConfig, // bottom-up conv + bu_conv2: BaseConvConfig, // bottom-up conv +} + +impl PafpnConfig { + /// Create a new instance of the PAFPN [config](PafpnConfig). + pub fn new(depth: f64, width: f64) -> Self { + assert!( + [0.33, 0.67, 1.0, 1.33].contains(&depth), + "invalid depth value {depth}" + ); + assert!( + [0.25, 0.375, 0.5, 0.75, 1.0, 1.25].contains(&width), + "invalid width value {width}" + ); + + let in_channels: [usize; 3] = [256, 512, 1024]; + let hidden_channels: [usize; 2] = [ + expand(2 * in_channels[0], width), + expand(2 * in_channels[1], width), + ]; + let in_channels: [usize; 3] = [ + expand(in_channels[0], width), + expand(in_channels[1], width), + expand(in_channels[2], width), + ]; + let num_blocks = (3_f64 * depth).round() as usize; + + let backbone = CspDarknetConfig::new(depth, width); + let lateral_conv0 = BaseConvConfig::new(in_channels[2], in_channels[1], 1, 1, 1); + let c3_p4 = + CspBottleneckConfig::new(hidden_channels[1], in_channels[1], num_blocks, 0.5, false); + + let reduce_conv1 = BaseConvConfig::new(in_channels[1], in_channels[0], 1, 1, 1); + let c3_p3 = + CspBottleneckConfig::new(hidden_channels[0], in_channels[0], num_blocks, 0.5, false); + + let bu_conv2 = BaseConvConfig::new(in_channels[0], in_channels[0], 3, 2, 1); + let c3_n3 = + CspBottleneckConfig::new(hidden_channels[0], in_channels[1], num_blocks, 0.5, false); + + let bu_conv1 = BaseConvConfig::new(in_channels[1], in_channels[1], 3, 2, 1); + let c3_n4 = + CspBottleneckConfig::new(hidden_channels[1], in_channels[2], num_blocks, 0.5, false); + + Self { + backbone, + lateral_conv0, + c3_n3, + c3_n4, + c3_p3, + c3_p4, + reduce_conv1, + bu_conv1, + bu_conv2, + } + } + + /// Initialize a new [PAFPN](Pafpn) module. + pub fn init(&self, device: &Device) -> Pafpn { + Pafpn { + backbone: self.backbone.init(device), + lateral_conv0: self.lateral_conv0.init(device), + c3_n3: self.c3_n3.init(device), + c3_n4: self.c3_n4.init(device), + c3_p3: self.c3_p3.init(device), + c3_p4: self.c3_p4.init(device), + reduce_conv1: self.reduce_conv1.init(device), + bu_conv1: self.bu_conv1.init(device), + bu_conv2: self.bu_conv2.init(device), + } + } + + /// Initialize a new [PAFPN](Pafpn) module with a [record](PafpnRecord). + pub fn init_with(&self, record: PafpnRecord) -> Pafpn { + Pafpn { + backbone: self.backbone.init_with(record.backbone), + lateral_conv0: self.lateral_conv0.init_with(record.lateral_conv0), + c3_n3: self.c3_n3.init_with(record.c3_n3), + c3_n4: self.c3_n4.init_with(record.c3_n4), + c3_p3: self.c3_p3.init_with(record.c3_p3), + c3_p4: self.c3_p4.init_with(record.c3_p4), + reduce_conv1: self.reduce_conv1.init_with(record.reduce_conv1), + bu_conv1: self.bu_conv1.init_with(record.bu_conv1), + bu_conv2: self.bu_conv2.init_with(record.bu_conv2), + } + } +} diff --git a/yolox-burn/src/model/weights.rs b/yolox-burn/src/model/weights.rs new file mode 100644 index 0000000..8de36b1 --- /dev/null +++ b/yolox-burn/src/model/weights.rs @@ -0,0 +1,128 @@ +/// Pre-trained weights metadata. +pub struct Weights { + pub(super) url: &'static str, + pub(super) num_classes: usize, +} + +#[cfg(feature = "pretrained")] +mod downloader { + use super::*; + use burn::data::network::downloader; + use std::fs::{create_dir_all, File}; + use std::io::Write; + use std::path::PathBuf; + + impl Weights { + /// Download the pre-trained weights to the local cache directory. + pub fn download(&self) -> Result { + // Model cache directory + let model_dir = dirs::home_dir() + .expect("Should be able to get home directory") + .join(".cache") + .join("yolox-burn"); + + if !model_dir.exists() { + create_dir_all(&model_dir)?; + } + + let file_base_name = self.url.rsplit_once('/').unwrap().1; + let file_name = model_dir.join(file_base_name); + if !file_name.exists() { + // Download file content + let bytes = downloader::download_file_as_bytes(self.url, file_base_name); + + // Write content to file + let mut output_file = File::create(&file_name)?; + let bytes_written = output_file.write(&bytes)?; + + if bytes_written != bytes.len() { + return Err(std::io::Error::new( + std::io::ErrorKind::InvalidData, + "Failed to write the whole model weights file.", + )); + } + } + + Ok(file_name) + } + } +} + +pub trait WeightsMeta { + fn weights(&self) -> Weights; +} + +/// YOLOX-S pre-trained weights. +pub enum YoloxTiny { + /// These weights were released after the original paper implementation with slightly better results. + /// mAP (val2017): 32.8 + Coco, +} +impl WeightsMeta for YoloxTiny { + fn weights(&self) -> Weights { + Weights { + url: "https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_tiny.pth", + num_classes: 80, + } + } +} + +/// YOLOX-S pre-trained weights. +pub enum YoloxS { + /// These weights were released after the original paper implementation with slightly better results. + /// mAP (test2017): 40.5 + Coco, +} +impl WeightsMeta for YoloxS { + fn weights(&self) -> Weights { + Weights { + url: "https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth", + num_classes: 80, + } + } +} + +/// YOLOX-M pre-trained weights. +pub enum YoloxM { + /// These weights were released after the original paper implementation with slightly better results. + /// mAP (test2017): 47.2 + Coco, +} +impl WeightsMeta for YoloxM { + fn weights(&self) -> Weights { + Weights { + url: "https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_m.pth", + num_classes: 80, + } + } +} + +/// YOLOX-L pre-trained weights. +pub enum YoloxL { + /// These weights were released after the original paper implementation with slightly better results. + /// mAP (test2017): 50.1 + Coco, +} +impl WeightsMeta for YoloxL { + fn weights(&self) -> Weights { + Weights { + url: "https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_l.pth", + num_classes: 80, + } + } +} + +/// YOLOX-X pre-trained weights. +pub enum YoloxX { + /// These weights were released after the original paper implementation with slightly better results. + /// mAP (test2017): 51.5 + Coco, +} +impl WeightsMeta for YoloxX { + fn weights(&self) -> Weights { + Weights { + url: "https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_x.pth", + num_classes: 80, + } + } +} diff --git a/yolox-burn/src/model/yolox.rs b/yolox-burn/src/model/yolox.rs new file mode 100644 index 0000000..30e606b --- /dev/null +++ b/yolox-burn/src/model/yolox.rs @@ -0,0 +1,289 @@ +use burn::{ + module::Module, + tensor::{backend::Backend, Device, Tensor}, +}; + +use super::{ + head::{Head, HeadConfig}, + pafpn::{Pafpn, PafpnConfig}, +}; + +#[cfg(feature = "pretrained")] +use { + super::weights::{self, WeightsMeta}, + burn::record::{FullPrecisionSettings, Recorder, RecorderError}, + burn_import::pytorch::{LoadArgs, PyTorchFileRecorder}, +}; + +// TODO: YOLOX +// Only YOLOX-Nano uses depthwise=True (DwsConv) + +/// [YOLOX](https://paperswithcode.com/method/yolox) object detection architecture. +#[derive(Module, Debug)] +pub struct Yolox { + backbone: Pafpn, + head: Head, +} + +impl Yolox { + pub fn forward(&self, x: Tensor) -> Tensor { + let features = self.backbone.forward(x); + self.head.forward(features) + } + + /// YOLOX-Tiny from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430). + /// + /// # Arguments + /// + /// * `num_classes`: Number of output classes of the model. + /// * `device` - Device to create the module on. + /// + /// # Returns + /// + /// A YOLOX-Tiny module. + pub fn yolox_tiny(num_classes: usize, device: &Device) -> Self { + YoloxConfig::new(0.33, 0.375, num_classes).init(device) + } + + /// YOLOX-Tiny from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430) + /// with pre-trained weights. + /// + /// # Arguments + /// + /// * `num_classes`: Number of output classes of the model. + /// * `device` - Device to create the module on. + /// + /// # Returns + /// + /// A YOLOX-Tiny module with pre-trained weights. + #[cfg(feature = "pretrained")] + pub fn yolox_tiny_pretrained( + weights: weights::YoloxTiny, + device: &Device, + ) -> Result { + let weights = weights.weights(); + let record = Self::load_weights_record(&weights, device)?; + + let model = YoloxConfig::new(0.33, 0.375, weights.num_classes).init_with(record); + + Ok(model) + } + + /// YOLOX-S from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430). + /// + /// # Arguments + /// + /// * `num_classes`: Number of output classes of the model. + /// * `device` - Device to create the module on. + /// + /// # Returns + /// + /// A YOLOX-S module. + pub fn yolox_s(num_classes: usize, device: &Device) -> Self { + YoloxConfig::new(0.33, 0.50, num_classes).init(device) + } + + /// YOLOX-S from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430) + /// with pre-trained weights. + /// + /// # Arguments + /// + /// * `num_classes`: Number of output classes of the model. + /// * `device` - Device to create the module on. + /// + /// # Returns + /// + /// A YOLOX-S module with pre-trained weights. + #[cfg(feature = "pretrained")] + pub fn yolox_s_pretrained( + weights: weights::YoloxS, + device: &Device, + ) -> Result { + let weights = weights.weights(); + let record = Self::load_weights_record(&weights, device)?; + + let model = YoloxConfig::new(0.33, 0.50, weights.num_classes).init_with(record); + + Ok(model) + } + + /// YOLOX-M from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430). + /// + /// # Arguments + /// + /// * `num_classes`: Number of output classes of the model. + /// * `device` - Device to create the module on. + /// + /// # Returns + /// + /// A YOLOX-M module. + pub fn yolox_m(num_classes: usize, device: &Device) -> Self { + YoloxConfig::new(0.67, 0.75, num_classes).init(device) + } + + /// YOLOX-M from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430) + /// with pre-trained weights. + /// + /// # Arguments + /// + /// * `num_classes`: Number of output classes of the model. + /// * `device` - Device to create the module on. + /// + /// # Returns + /// + /// A YOLOX-M module with pre-trained weights. + #[cfg(feature = "pretrained")] + pub fn yolox_m_pretrained( + weights: weights::YoloxM, + device: &Device, + ) -> Result { + let weights = weights.weights(); + let record = Self::load_weights_record(&weights, device)?; + + let model = YoloxConfig::new(0.67, 0.75, weights.num_classes).init_with(record); + + Ok(model) + } + + /// YOLOX-L from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430). + /// + /// # Arguments + /// + /// * `num_classes`: Number of output classes of the model. + /// * `device` - Device to create the module on. + /// + /// # Returns + /// + /// A YOLOX-L module. + pub fn yolox_l(num_classes: usize, device: &Device) -> Self { + YoloxConfig::new(1., 1., num_classes).init(device) + } + + /// YOLOX-L from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430) + /// with pre-trained weights. + /// + /// # Arguments + /// + /// * `num_classes`: Number of output classes of the model. + /// * `device` - Device to create the module on. + /// + /// # Returns + /// + /// A YOLOX-L module with pre-trained weights. + #[cfg(feature = "pretrained")] + pub fn yolox_l_pretrained( + weights: weights::YoloxL, + device: &Device, + ) -> Result { + let weights = weights.weights(); + let record = Self::load_weights_record(&weights, device)?; + + let model = YoloxConfig::new(1., 1., weights.num_classes).init_with(record); + + Ok(model) + } + + /// YOLOX-X from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430). + /// + /// # Arguments + /// + /// * `num_classes`: Number of output classes of the model. + /// * `device` - Device to create the module on. + /// + /// # Returns + /// + /// A YOLOX-X module. + pub fn yolox_x(num_classes: usize, device: &Device) -> Self { + YoloxConfig::new(1.33, 1.25, num_classes).init(device) + } + + /// YOLOX-X from [`YOLOX: Exceeding YOLO Series in 2021`](https://arxiv.org/abs/2107.08430) + /// with pre-trained weights. + /// + /// # Arguments + /// + /// * `num_classes`: Number of output classes of the model. + /// * `device` - Device to create the module on. + /// + /// # Returns + /// + /// A YOLOX-X module with pre-trained weights. + #[cfg(feature = "pretrained")] + pub fn yolox_x_pretrained( + weights: weights::YoloxX, + device: &Device, + ) -> Result { + let weights = weights.weights(); + let record = Self::load_weights_record(&weights, device)?; + + let model = YoloxConfig::new(1.33, 1.25, weights.num_classes).init_with(record); + + Ok(model) + } + + /// Load specified pre-trained PyTorch weights as a record. + fn load_weights_record( + weights: &weights::Weights, + device: &Device, + ) -> Result, RecorderError> { + // Download torch weights + let torch_weights = weights.download().map_err(|err| { + RecorderError::Unknown(format!("Could not download weights.\nError: {err}")) + })?; + + // Load weights from torch state_dict + let load_args = LoadArgs::new(torch_weights) + // State dict contains "model", "amp", "optimizer", "start_epoch" + .with_top_level_key("model") + // Map backbone.C3_* -> backbone.c3_* + .with_key_remap("backbone\\.C3_(.+)", "backbone.c3_$1") + // Map backbone.backbone.dark[i].0.* -> backbone.backbone.dark[i].conv.* + .with_key_remap("(backbone\\.backbone\\.dark[2-5])\\.0\\.(.+)", "$1.conv.$2") + // Map backbone.backbone.dark[i].1.* -> backbone.backbone.dark[i].c3.* + .with_key_remap("(backbone\\.backbone\\.dark[2-4])\\.1\\.(.+)", "$1.c3.$2") + // Map backbone.backbone.dark5.1.* -> backbone.backbone.dark5.spp.* + .with_key_remap("(backbone\\.backbone\\.dark5)\\.1\\.(.+)", "$1.spp.$2") + // Map backbone.backbone.dark5.2.* -> backbone.backbone.dark5.c3.* + .with_key_remap("(backbone\\.backbone\\.dark5)\\.2\\.(.+)", "$1.c3.$2") + // Map head.{cls | reg}_convs.x.[i].* -> head.{cls | reg}_convs.x.conv[i].* + .with_key_remap( + "(head\\.(cls|reg)_convs\\.[0-9]+)\\.([0-9]+)\\.(.+)", + "$1.conv$3.$4", + ); + let record = PyTorchFileRecorder::::new().load(load_args, device)?; + + Ok(record) + } +} + +/// [YOLOX detector](Yolox) configuration. +pub struct YoloxConfig { + backbone: PafpnConfig, + head: HeadConfig, +} + +impl YoloxConfig { + /// Create a new instance of the YOLOX detector [config](YoloxConfig). + pub fn new(depth: f64, width: f64, num_classes: usize) -> Self { + let backbone = PafpnConfig::new(depth, width); + let head = HeadConfig::new(num_classes, width); + + Self { backbone, head } + } + + /// Initialize a new [YOLOX detector](Yolox) module. + pub fn init(&self, device: &Device) -> Yolox { + Yolox { + backbone: self.backbone.init(device), + head: self.head.init(device), + } + } + + /// Initialize a new [YOLOX detector](Yolox) module with a [record](YoloxRecord). + pub fn init_with(&self, record: YoloxRecord) -> Yolox { + Yolox { + backbone: self.backbone.init_with(record.backbone), + head: self.head.init_with(record.head), + } + } +} From 5d60738ab2d820617273214afa21371a054a779e Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Fri, 8 Mar 2024 10:34:16 -0500 Subject: [PATCH 02/21] Fix 2d grid for anchors --- yolox-burn/src/model/head.rs | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/yolox-burn/src/model/head.rs b/yolox-burn/src/model/head.rs index 73c7606..41c9777 100644 --- a/yolox-burn/src/model/head.rs +++ b/yolox-burn/src/model/head.rs @@ -23,12 +23,12 @@ const PRIOR_PROB: f64 = 1e-2; /// but specific to two dimensions. fn create_2d_grid(x: usize, y: usize, device: &Device) -> Tensor { let y_idx = Tensor::arange(0..y as i64, device) - .repeat(0, x) + .reshape(Shape::new([y, 1])) + .repeat(1, x) .reshape(Shape::new([y, x])); let x_idx = Tensor::arange(0..x as i64, device) .repeat(0, y) - .reshape(Shape::new([x, y])) - .transpose(); + .reshape(Shape::new([x, y])); Tensor::stack(vec![x_idx, y_idx], 2) } From 49ac306ae78511a7afacc379cb7a08449402d5e3 Mon Sep 17 00:00:00 2001 From: Guillaume Lagrange Date: Fri, 8 Mar 2024 10:48:26 -0500 Subject: [PATCH 03/21] Change sample image --- yolox-burn/NOTICES.md | 8 ++++---- yolox-burn/samples/dog.jpg | Bin 4055302 -> 0 bytes yolox-burn/samples/dog_bike_man.jpg | Bin 0 -> 39085 bytes 3 files changed, 4 insertions(+), 4 deletions(-) delete mode 100644 yolox-burn/samples/dog.jpg create mode 100644 yolox-burn/samples/dog_bike_man.jpg diff --git a/yolox-burn/NOTICES.md b/yolox-burn/NOTICES.md index 776afb6..c3aac0c 100644 --- a/yolox-burn/NOTICES.md +++ b/yolox-burn/NOTICES.md @@ -4,10 +4,10 @@ This file contains notices and information required by libraries that this repos ## Sample Image -Image Title: This is our Mascot DeVille. -Author: Adelaidasofia -Source: https://commons.wikimedia.org/wiki/File:VillyCustomsMascotDeVille%22Villy%22.jpg -License: https://creativecommons.org/licenses/by-sa/3.0/ +Image Title: Man with Bike and Pet Dog circa 1900 (archive ref DDX1319-2-3) +Author: East Riding Archives +Source: https://commons.wikimedia.org/wiki/File:Man_with_Bike_and_Pet_Dog_circa_1900_%28archive_ref_DDX1319-2-3%29_%2826507570321%29.jpg +License: [Creative Commons](https://www.flickr.com/commons/usage/) ## Pre-trained Model diff --git a/yolox-burn/samples/dog.jpg b/yolox-burn/samples/dog.jpg deleted file mode 100644 index f748a5da0f46c2b7fc349c0326a9e542ab76f4ec..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 4055302 zcmeFY1z1*1zc0LQxakrE1w}$YQW^xLI|QT~q`SKW5ew-QBt$?OM7l(!yBk5eLAv40 ztv)`!&%5_N-~P_I&Uc;b`UYmrZ~fS$`3jOeS=;9jhA3_zxaV>7~5|> z-(l=OFyRkX{D0A5gh-eF=6_}h!GG$6Q9}>`8O47cAO#sYMFmJqQSsh=8BGcmHdadLR$Hq&+#?Hsi$;ZhF$r~C|D5_9s 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